While semi-asynchronous federated learning (SAFL) combines the efficiency of synchronous training with the flexibility of asynchronous updates, it inherently suffers from participation bias, which is further exacerbated by non-IID data distributions. More importantly, hierarchical architecture shifts participation from individual clients to client groups, thereby further intensifying this issue. Despite notable advancements in SAFL research, most existing works still focus on conventional cloud-end architectures while largely overlooking the critical impact of non-IID data on scheduling across the cloud–edge–client hierarchy. To tackle these challenges, we propose FedCure, an innovative semiasynchronous Federated learning framework that leverages Coalition construction and participation-aware scheduling to mitigate participation bias with non-IID data. Specifically, FedCure operates through three key rules: (1) a preference rule that optimizes coalition formation by maximizing collective benefits and establishing theoretically stable partitions to reduce non-IID-induced performance degradation; (2) a scheduling rule that integrates the virtual queue technique with Bayesian-estimated coalition dynamics, mitigating efficiency loss while ensuring mean rate stability; and (3) a resource allocation rule that enhances computational efficiency by optimizing client CPU frequencies based on estimated coalition dynamics while satisfying delay requirements. Comprehensive experiments on four real-world datasets demonstrate that FedCure improves accuracy by up to 5.1x compared with four state-of-the-art baselines, while significantly enhancing efficiency with the lowest coefficient of variation 0.0223 for per-round latency and maintaining long-term balance across diverse scenarios.

Asynchronous Federated Learning (AFL) is acclaimed for accelerating collaborative training on heterogeneous systems by eliminating the wait for stragglers. While current solutions focus on improving convergence amidst update delays, they neglect how delayed aggregation fosters free-riding attacks, allowing malicious clients to easily extract the global model without contribution. This behavior results in significant fairness issues and performance degradation. To address this challenge, we propose OPTION, the first online pricing strategy tailored to mitigate free-riding in AFL. OPTION establishes an economic model in which access to model updates is purchased using credits earned from verified contributions. Specifically, OPTION values each model update according to its marginal performance gain and training cost, and subsequently necessitates a download fee from each client based on the Hotelling model to prevent zero-cost acquisition. Moreover, OPTION rewards clients for successful updates under non-arbitrage constraints, effectively balancing individual utility and task budget. To maximize the average model performance while satisfying these conditions, OPTION leverages the Lyapunov drift framework and a probabilistic sampling-based algorithm to optimize the pricing parameters. Extensive experimental results on three real-world datasets demonstrate that OPTION effectively mitigates freeriding attacks in AFL, increases the number of valid updates by at least 23.97%, and achieves a model accuracy improvement of at least 3.01% compared to state-of-the-art baselines.

With the increasing application of high-stakes decisionmaking application in Federated Learning (FL), ensuring fairness across different populations to prevent biases against certain groups has become crucial. However, achieving group fairness (GF) in FL presents a formidable challenge due to its decentralization, which complicates the global GF estimation by the server. Moreover, distrust and fragility hinder the server from gathering GF values from unreliable clients. This challenge motivates our proposal of OursFed, a provable GF-aware FL framework that integrates a privacy pairbased contract and robust GF estimation method to address issues of distrust and fragility. Methodologically, we categorize client unreliability into two categories: active unreliability stemming from distrust and passive unreliability arising from fragility. To mitigate active unreliability, we design a privacy pair-based contract to guarantee truthful GF reporting, and enhance multivariate analysis by identifying relationships among multiple private data. To counteract passive unreliability, we develop a robust GF estimation using non-parametric techniques to smooth data and estimate probability densities and regression functions, improving per-client GF accuracy under multi-dimensional data perturbation. Theoretically, we demonstrate the efficacy of OursFed by analyzing its convergence, GF stability, and accuracy deviation. Experimentally, evaluations on two real datasets show that OursFed improves GF by 28.61% with at most 2.7% trade-off versus state-ofthe-art baselines, and synthetic experiments further confirm its effectiveness in handling fragility and distrust.

Contribution evaluation is essential for incentivizing high-quality data sharing in federated learning (FL), yet existing Shapley-value-based methods are prohibitively expensive and overlook temporal influence propagation. In this paper, we propose Ripple Shapley, a novel attribution framework that enables accurate, real-time data valuation within a single federated training run. Our method decomposes each sample’s impact into an instantaneous drop term and a recursive ripple term, the latter capturing downstream influence via a Jacobian chain over global updates. To scale computation, we introduce a low-rank approximation of the Jacobian product and construct a shared subspace for efficient ripple accumulation. Extensive experiments on CIFAR-10 and MNIST show that Ripple Shapley achieves up to 62× speedup over existing Shapley-based FL methods while maintaining high attribution fidelity, significantly improving efficiency, robustness, and fairness in federated environments. We further demonstrate its effectiveness in dynamic federated learning scenarios and its potential for real-time data pricing.

Accurate dynamic modeling of task correlations is crucial for enhancing collaborative efficiency and personalized performance in federated multi-task learning (FML), yet existing approaches struggle with heterogeneous environments due to static assumptions or implicit modeling. Moreover, task relationships typically remain implicit, embedded within data distributions and parameter variations, making precise modeling inherently challenging. This challenge is further intensified in Non-IID settings, where data heterogeneity impedes both the identification and accurate estimation of task relationships. To address these challenges, we propose FedSame, a similarity-aware FML framework that leverages Bayesian inference to dynamically model inter-task relationships. The key innovation of FedSame lies in its probabilistic reformulation of task relationship modeling, where an adaptive similarity matrix undergoes continuous Bayesian updates to precisely track evolving task relationships. FedSame’s technical core combines Beta distribution priors with Bayesian update rules, enabling fine-grained detection of subtle variations in task relationship variations during training, and computationally efficient dynamic updates by leveraging the conjugacy property of the Beta distribution. Extensive experiments on two real datasets and a synthetic dataset demonstrate that FedSame consistently outperforms five state-of-the-art baselines in both task relationship modeling accuracy and multi-task classification performance. Remarkably, FedSame attains 73% accuracy in multi-attribute classification on CelebA and 85% accuracy in task relationship modeling on synthetic data, all while maintaining robust performance and notable adaptability in heterogeneous federated environments.

With the rapid growth of data, redundancy among different users in cloud environments has become increasingly prominent. Detecting and removing these redundant parts can effectively improve storage efficiency. But these processes may dramatically degrade the system performance, especially when dealing with similar data. Although deduplication and delta compression are common data reduction techniques, their high overhead can outweigh the benefits. As a result, users often cannot determine in advance whether compression is worthwhile for their datasets. Some approaches have attempted to solve this, but each has important limitations. Danny Harnik et al. proposed a sampling-based deduplication estimation method using linear programming, which efficiently estimates redundancy from exact duplicates. However, it fails to capture redundancy arising from similar data, thus underestimating the full compression potential. To address this limitation, we propose Smart-to-Compress, a predictive compression decision framework. We introduce the Super Feature Frequency Histogram (SFH) to capture redundancy among similar data. Combined with the Duplication Frequency Histogram (DFH), our method estimates the overall Data Reduction Ratio (DRR) without scanning the entire dataset. Furthermore, we design a game-theoretic decision model to weigh compression benefits against predicted costs, providing users with guidance on whether compression should be applied. Experiments on real-world datasets show that our method accurately predicts compression value, reduces unnecessary overhead, and offers reliable decision-making support for users.

Federated learning (FL) facilitates privacy-preserving collaborative intelligence, making it ideal for mobile edge computing (MEC)-enabled Industrial Internet of Thing (IIoT). However, the autonomy of participants leads to unstable edge associations, hampering FL deployment. Existing studies typically prioritize device incentives but overlook price competition and network congestion at the server level. To tackle these issues, we propose coMpetition sOftening and coNgestion mItigation (MONI), a communication-efficient incentive mechanism for service pricing. Specifically, MONI employs a dynamic multiteam Bertrand game model to capture boundedly rational interactions among edge servers. It leverages capacity constraints to alleviate price competition and mitigate network congestion, while preserving the uniform stability of the game. Furthermore, MONI incorporates a genetic algorithm augmented with a truncated Gaussian distribution to minimize the disconnection of roaming devices. Experiments on synthetic and real-world industrial datasets demonstrate that MONI reduces recruitment costs and network congestion, increases the number of devices by 18.37%, and boosts model performance by up to 6.39% compared to state-of-the-art benchmarks.

Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally. However, dynamic mobile networks with high mobility, intermittent connectivity, and bandwidth limitation severely hinder model updates to the cloud server. Although previous studies have typically addressed user mobility issue through task reassignment or predictive modeling, frequent migrations may result in high communication overhead. Addressing this challenge involves not only dealing with resource constraints, but also finding ways to mitigate the challenges posed by user migrations. We therefore propose a intertemporal incentive framework, FedCross, which ensures the continuity of FL tasks by migrating interrupted training tasks to feasible mobile devices. FedCross comprises two distinct stages: Specifically, in Stage 1, we address the task allocation problem across regions under resource constraints by employing a multi-objective migration algorithm to quantify the optimal task receivers. Moreover, we adopt evolutionary game theory to capture the dynamic decision-making of users, forecasting the evolution of user proportions across different regions to mitigate frequent migrations. In Stage 2, we utilize a procurement auction mechanism to allocate rewards among base stations, ensuring that those providing high-quality models receive optimal compensation. This approach incentivizes sustained user participation, thereby ensuring the overall feasibility of FedCross. Finally, experimental results validate the theoretical soundness of FedCross and demonstrate its significant reduction in communication overhead.

Due to the sensitivity of data, Federated Learning (FL) is employed to enable distributed machine learning while safeguarding data privacy and accommodating the requirements of various devices. However, in the context of semidecentralized FL, clients’ communication and training states are dynamic. This variability arises from local training fluctuations, heterogeneous data distributions, and intermittent client participation. Most existing studies primarily focus on stable client states, neglecting the dynamic challenges inherent in real-world scenarios. To tackle this issue, we propose a TRust-Aware clIent scheduLing mechanism called TRAIL, which assesses client states and contributions, enhancing model training efficiency through selective client participation. We focus on a semi-decentralized FL framework where edge servers and clients train a shared global model using unreliable intra-cluster model aggregation and inter-cluster model consensus. First, we propose an adaptive hidden semi-Markov model to estimate clients’ communication states and contributions. Next, we address a client-server association optimization problem to minimize global training loss. Using convergence analysis, we propose a greedy client scheduling algorithm. Finally, our experiments conducted on real-world datasets demonstrate that TRAIL outperforms state-of-the-art baselines, achieving an improvement of 8.7% in test accuracy and a reduction of 15.3% in training loss.

Online Federated Learning (OFL) is a real-time learning paradigm that sequentially executes parameter aggregation immediately for each random arriving client. To motivate clients to participate in OFL, it is crucial to offer appropriate incentives to offset the training resource consumption. However, the design of incentive mechanisms in OFL is constrained by the dynamic variability of Two-sided Incomplete Information (TII) concerning resources, where the server is unaware of the clients’ dynamically changing computational resources, while clients lack knowledge of the real-time communication resources allocated by the server. To incentivize clients to participate in training by offering dynamic rewards to each arriving client, we design a novel Dynamic Bayesian persuasion pricing for online Federated learning (DaringFed) under TII. Specifically, we begin by formulating the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, and then demonstrate the existence of a unique Bayesian persuasion Nash equilibrium. By deriving the optimal design of DaringFed under one-sided incomplete information, we further analyze the approximate optimal design of DaringFed with a specific bound under TII. Finally, extensive evaluation conducted on real datasets demonstrate that DaringFed optimizes accuracy and converges speed by 16.99%, while experiments with synthetic datasets validate the convergence of estimate unknown values and the effectiveness of DaringFed in improving the server’s utility by up to 12.6%.

Although federated learning (FL) enables collaborative training across multiple decentralized devices, strategic clients may be reluctant to participate in FL unless sufficient incentives are available. Existing researches predominantly emphasize short-term incentives, while FL model training is typically a long-term process, and malicious clients may exhibit dishonest behavior during local training. Although designing a long-term incentive mechanism is crucial for FL, this task is challenging due to the heterogeneous nature of FL and the limitations of imperfect system monitoring. To this end, this article designs the first sustainable incentive mechanism for FL called RATE, which aims to incentivize clients to continuously contribute more high-quality data. Specifically, by modeling the competition and cooperation relationship between servers and clients as a multiserver multiclient Stackelberg game, we prove the existence and uniqueness of the Stackelberg equilibrium (SE), and derive the unique SE through the cautious calculation of designed algorithms. Since the derived SE may not be optimal, we further utilize reputation to measure the long-term contribution of clients and build a function between their revenues and reputations to ensure the optimal revenue allocation, thereby maximizing the social welfare of RATE. Extensive experiments on both synthetic and real data sets demonstrate the superiority of RATE. Compared to the state-of-the-art baselines, RATE increases the total utility of the servers by up to 20%, reduces the reputation of malicious clients by up to 90%, and improves the average test accuracy overall.

Although promising, federated edge learning (FEL) is being plagued by unreliable clients with low-quality parameters due to tight edge association and frequent edge aggregation. Existing efforts mainly focus on setting thresholds or identifying malicious behaviors to resist unreliable clients, which comes at the cost of losing their training samples and leads to unsustainable and collaborative inefficiencies. To tackle this issue, we propose the first sustainable contract, named FedSC, which allows for sustaining truthful contributions in more general conditions including clients’ multidimensional attributes and imperfect system monitoring. Specifically, by modeling the long-term strategic behaviors of self-interested clients as a Markov decision process, we quantify the impact of client behavior on their utilities and derive the critical conditions that make the rating-based contract sustainable, thereby promoting honest participation as the optimal choice for strategic clients. Since directly deriving the optimal design of FedSC under multiple constraints and nonlinear coupling of parameters is intractable, we characterize the impact of design parameters on objective function and analytically prove the existence of closed solution. Then, through a low-time-complexity greedy-based algorithm, the optimality of sustainable contracts under different system errors is guaranteed. Extensive experiments using both synthetic and real datasets demonstrate the effectiveness and superiority of FedSC compared to the state-of-the-art baselines. Excitingly, FedSC can reduce the number of free-riders up to 34.52% and improve the amount of contributed data and model performance up to 22.98% and 8.62%, respectively.

Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks. Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements. Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures. To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method. Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure. Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16 KB or more, outperforming state-of-the-art methods.

Although Federated Learning (FL) offers significant potential for developing model marketplaces through collaborative training and privacy preservation, challenges such as insufficient training data and arbitrage issues severely impede the development of FL-based model marketplaces. Existing studies either lack satisfactory security guarantees or are too profit-driven to address potential arbitrage issues. In this paper, we propose a novel Personalized prIvacy-prEserving inCentive mEchanism named PIECE, with the aim of achieving social optimality while avoiding arbitrage. Specifically, we first formulate a dual-objective optimization problem to simultaneously maximize social utility and model performance while ensuring arbitrage-free conditions through differential privacy. Due to dynamic model training and heterogeneous privacy budgets that complicate the design of arbitrage-free properties, we model the transformation between local and global privacy requirements across scenarios as a privacy choice game. This game guarantees the identification of a constraint to generate desired model versions based on Nash equilibrium. Next, by generalizing the properties of different data-owner groups under equilibrium conditions, we prove that the dual-objective optimization problem is always conflict-free, thus allowing transformation into a social optimal problem without arbitrage. Furthermore, to tackle the significant difficulty in characterizing the model revenue and interpolating pricing, we propose a two-stage solution based on subadditivity relaxation. The first stage establishes a set of ideal prices as the target, while the second stage establishes polynomial-time solvability and provides rigorous arbitrage-free boundaries. Finally, comprehensive experiments on four real-world datasets validate the efficacy of PIECE. The results indicate a minimum 8% boost in model revenue within the specified marketplace scale, and a maximum 16.67% improvement in model performance compared to the state-of-the-art baselines.

In the operational context of a cross-device federated learning (FL), the efficient allocation of resources, such as transmission powers, channels, and computation resources, significantly impacts overall performance. Existing research in cross-device FL has predominantly concentrated on either resource allocation to enhance training accuracy or incentivizing participation, while ignoring their integrated designs for further improving the performance in cross-device FL. Different from existing work, in this paper, we jointly integrate the power allocation, channel assignment, user selection, and allocation of computation frequency into the design of incentive mechanism, where each mobile user plays a dual role as both a buyer and a seller. Because of complex resource allocation, truthfulness guarantee in a dual role scenario, and unavailable prior information, the considered mechanism design problem is challenging. To tackle such combinatorial problem, we propose a Reinforcement Auction Mechanism (RAM), comprising two layers. The upper layer features a Hybrid Action Reinforcement Learning scheme to learn the outcomes of user selection and payments. In the lower layer, each selected mobile user optimizes its resources to maximize its utility. Theoretical analyses affirm that our proposed RAM ensures individual rationality and truthfulness. Extensive simulations have been conducted to validate the effectiveness of the proposed RAM.

Devices and servers in Federated Edge Learning (FEL) are self-interested and resource-constrained, making it critical to design incentives to improve model performance. However, dynamic network conditions raise energy consumption, while data heterogeneity undermines device cooperation. Current research overlooks the interplay between system efficiency and device clustering, resulting in suboptimal updates. To address these challenges, we develop BENCH, a bilateral pricing mechanism consisting of three core rules aimed at incentivizing participation from both devices and servers. Specifically, we first design a reward allocation rule, based on the Rubinstein bargaining model, which dynamically allocates rewards. Theoretically, we derive a closed-form solution for this rule, demonstrating BENCH achieves Nash equilibrium. Secondly, we design a device partitioning rule that leverages modularity to group similar devices, facilitating personalized edge aggregation to accelerate local data adaptation. Thirdly, we design an edge matching rule that employs the Kuhn-Munkres algorithm to balance the load at edge servers, thus minimizing the congestion. Together, these three rules enable hierarchical optimization of pricing and associations, effectively mitigating the impact of dynamic costs and device heterogeneity. Extensive experiments demonstrate BENCH's effectiveness in increasing device participation by 28.81% and improving model performance by 2.66% compared to state-of-the-art baselines.

Cellular traffic prediction provides significant support for the management of intelligent networks. Existing models commonly combine recurrent neural networks (RNNs) with attention mechanisms, convolutional neural networks (CNNs), or graph convolutional networks (GCNs) to capture spatial-temporal correlations of cellular traffic. However, attention mechanisms lack sensitivity to local information; CNNs ignore the interaction among distant regions with similar semantics; GCNs exhibit limitations in exploring high-order (beyond pairwise) spatial correlations. To this end, we develop a hypergraph attention recurrent network (HARN) that exploits locality, semantics, and high-order correlations for cellular traffic prediction. Specifically, we first propose a spatial trend-aware attention to perceive local trends, thus easing the mismatching problem of attention mechanisms. Then, we construct a hypergraph to characterize the interactions between distant regions with similar semantics, and leverage a hypergraph convolution network to extract high-order correlations. More importantly, to extract heterogeneous and varying spatial patterns, we further enhance the hypergraph convolution network by incorporating spatial-temporal representations. Last, extensive experiments on three real-world datasets demonstrate the superiority of HARN over state-of-the-art baselines in terms of mean absolute error and root mean square error, with specific improvements of 1.83% and 5.79% on SMS (short message service) dataset, 3.05% and 11.27% on Call dataset, and 1.36% and 1.65% on Internet dataset, respectively.

Hierarchical Federated Learning (HFL) improves the scalability and communication efficiency of the system and achieves load balancing at each level. Incentive mechanisms enhance participant motivation and optimize resource allocation for HFL. However, existing mechanisms mainly focus on maximizing individual utility from the quantity of client data while neglecting to optimize social utility from the learning quality perspective. Meanwhile, strategic behavior and heterogeneous devices can significantly degrade the performance of incentive mechanisms. To this end, we propose a quality-aware incentive mechanism (QAIM) for HFL to improve training efficiency. Specifically, we first systematically evaluate the learning quality of clients based on their training loss and historical records, which allows us to recruit high-quality clients for model updating selectively. Then, we model the cloud-edge-end interaction and cooperation as a three-layer Stackelberg game to analyze the strategies of participants and utilize carefully designed algorithms to derive the solution of the unique Stackelberg Equilibrium (SE). Through the Pareto improvement of client association modeled as a coalition game, we can maximize social utility. Experimental results on both synthetic and real-world datasets demonstrate that our QAIM outperforms the state-of-the-art baselines, with an average increase in accuracy and social utility of 17% and 45%, respectively.

Although federated learning (FL) enables collaborative training across multiple data silos in a privacy-protected manner, naively minimizing the aggregated loss to facilitate an efficient federation may compromise its fairness. Many efforts have been devoted to maintaining similar average accuracy across clients by reweighing the loss function while clients’ potential contributions are largely ignored. This, however, is often detrimental since treating all clients equally will harm the interests of those clients with more contribution. To tackle this issue, we introduce utopian fairness to expound the relationship between individual earning and collaborative productivity, and proposeFederated-UtoPia (FedUP), a novel FL framework that balances both efficient collaboration and fair aggregation. For the distributed collaboration, we model the training process among strategic clients as a supermodular game, which facilitates a rational incentive design through the optimal reward. As for the model aggregation, we design a weight attention mechanism to compute the fair aggregation weights by minimizing the performance bias among heterogeneous clients. Particularly, we utilize the alternating optimization theory to bridge the gap between collaboration efficiency and utopian fairness, and theoretically prove that FedUP has fair model performance with fast-rate training convergence. Extensive experiments using both synthetic and real datasets demonstrate the superiority of FedUP.

Effective incentive mechanisms are invaluable in crowdsensing to stimulate the enthusiasm of strategic users. However, existing work focusing on multi-task allocation with the objective of purely maximizing the social utility may result in the problem of unbalanced allocation, which may damage the social fairness. This motivates us to introduce proportional fairness into the design of a novel fairness-aware incentive mechanism for the first time. Specifically, we first model the interaction of multi-task allocation in crowdsensing as a multi-requester multi-worker Stackelberg game, and then transform the fairness-aware multi-task allocation problem into a fairness-aware incentive mechanism design problem. Next, we prove that there is a unique Stackelberg equilibrium, and also show that it can be efficiently derived through cautiously proposed algorithms. Since the existing equilibrium may not be optimal, we further design a secondary allocation rule to maximize both social utility and system performance, while achieving proportional fairness at a minimum cost. Finally, extensive experiments using both synthetic and real-world datasets demonstrate the superiority of our proposed mechanism compared to the state of the arts.

With the raised privacy concerns and rigorous data regulations, federated learning has become a hot collaborative learning paradigm for the recommendation model without sharing the highly sensitive POI data. However, the time-sensitive, heterogeneous, and limited POI records seriously restrict the development of federated POI recommendation. To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. In details, PrefFedPOI extracts the fine-grained preference of current time slot by combining historical recent preferences and periodic preferences within each local client. Due to the extreme lack of POI data in some time slots, a data amount aware selective strategy is designed for model parameters uploading. Moreover, a performance enhanced clustering mechanism with reinforcement learning is proposed to capture the preference relatedness among all clients to encourage the positive knowledge sharing. Furthermore, a clustering teacher network is designed for improving efficiency by clustering guidance. Extensive experiments are conducted on two diverse real-world datasets to demonstrate the effectiveness of proposed PrefFedPOI comparing with state-of-the-arts. In particular, personalized PrefFedPOI can achieve 7% accuracy improvement on average among data-sparsity clients.

Energy harvesting technology enables wireless sensor networks (WSNs) to be self-sustainable, for maintaining long-term key performance indicators, such as the data throughput and sensing coverage. Due to the highly dynamic and complex environment, energy sources (ES) cannot provide stable energy supply, which needs the efficient learning algorithm to enable system adaptations. This article reports on the development of reinforcement learning (RL) methodology to long-term data collection in self-sustainable WSNs. Specifically, we consider the WSN as a 2-D rectangular region, where a mobile sensor (MS) can harvest energy from ambient environments while transmitting the collected data to a fixed sink. Due to the changing environment and the mobility of the MS, the harvested energy by the MS at each slot presents spatiotemporal dynamics within the network, which severely affects the performance of data throughput from the MS to the sink. The MS’s trajectory is investigated to maximize the long-term average MS-to-sink data throughput. Due to the unknown energy arrival information as well as the locations of ESs, we formulate the problem as a Markov decision process, which is then solved with model-free RL. In particular, the deep deterministic policy gradient (DDPG) is applied to tackle the continuous and deterministic movement space. Results show that the MS can learn and optimize the moving trajectory by intelligently tracking the aggregated received energy over slots. Finally, the MS can identify and move to the optimal location where the maximized long-term average MS-to-sink data throughput is achieved. Extensive numerical evaluations are conducted to investigate the impact of various system parameters on the network performance.

Although vertical federated learning (VFL) has become a new paradigm of distributed machine learning for emerging multiparty joint modeling applications, how to effectively incentivize self-conscious clients to actively and reliably contribute to collaborative learning in VFL has become a critical issue. Existing efforts are inadequate to address this issue since the training sample size needs to be unified before model training in VFL. To this end, selfish clients should unconditionally and honestly declare their private information, such as model training costs and benefits. However, such an assumption is unrealistic. In this article, we develop the first Truthful incEntive mechAnism for VFL,$\mathbb {TEA}$, to handle both information self-disclosure and social utility maximization. Specifically, we design a transfer payment rule via internalizing externalities, which bundles the clients’ utilities with the social utility, making truthful reporting by clients be a Nash equilibrium. Theoretically, we prove that$\mathbb {TEA}$can achieve truthfulness and social utility maximization, as well as budget balance (BB) or individual rationality (IR). On this basis, we further design a sample size decision rule via linear programming (LP) relaxation to meet the requirements of different scenarios. Finally, extensive experiments on synthetic and real-world datasets validate the theoretical properties of$\mathbb {TEA}$and demonstrate its superiority compared with the state-of-the-art.

Despite the rapid growth of successful examples of Federated Learning (FL), it faces the heterogeneity of data, models, and devices in emerging applications of Industrial Internet of Things (IIoT). Existing efforts mainly focus on training multiple personalized models by adopting a global, cluster, or pairwise fashion. However, the global collaboration does not work well in case of the non-IID data distribution, cluster collaboration is often inefficient due to the single cluster pattern and high computational cost, and pairwise collaboration incurs the limitations of collaboration scope and communication efficiency. To address the problems, we propose a novel personalized FL (PFL) framework with the game-theoretic insights, called group collaboration, to overcome the shortcomings of status quo. Specifically, we first formulate the group collaboration in PFL as a multileader multifollower Stackelberg game, and then develop an$\epsilon$-better response to efficiently characterize its unique equilibrium through cautiously proposing a potential function. Since the existing equilibrium may not be optimal, we further design a Robin Hood mechanism by using the idea of transferable utility to improve the performance of the training model. Meanwhile, we also prove that the new mechanism is sustainable and can converge to a stable state with an upper bound of the training loss. Last, extensive experiments on a simulated dataset and four real-world datasets demonstrate the superiority of our proposed approach compared to the state of the art.

Mobile chargers (MCs) are usually dispatched to deliver energy to sensors in wireless rechargeable sensor networks (WRSNs) due to its flexibility and easy maintenance. This paper concerns the fundamental issue of charging path DEsign with the Minimized energy cOst (DEMO), i.e., given a set of rechargeable sensors, we appropriately design the MC’s charging path to minimize the energy cost which is due to the wireless charging and the MC’s movement, such that the different charging demand of each sensor is satisfied. Solving DEMO is NP-hard and involves handling the tradeoff between the charging efficiency and the moving cost. To address DEMO, we first investigate how to identify a single charging position where the MC could stay to charge a set of sensors distributed within a small area with the maximized charging efficiency. Then, based on the result obtained in the case of optimizing a single charging position, we develop a computational geometry-based algorithm to deploy multiple charging positions within the whole network, by considering the fixed and finite charging range of the MC. We prove that the designed algorithm has the approximation ratio of$O\!\left ({\ln \!N}\right)$, where$N$is the number of sensors. Then we construct the charging path by calculating the shortest Hamiltonian cycle passing through all the deployed charging positions within the network. In addition, we investigate the impact of the network topology as well as the distribution of charging demands among sensors on the MC’s energy cost during a charging tour. Extensive evaluations validate the superiority of our path design in terms of the MC’s energy cost minimization, compared with existing main algorithms.

Mobile chargers (MCs) are usually dispatched to deliver energy to sensors in wireless rechargeable sensor networks (WRSNs) due to its flexibility and easy maintenance. This paper concerns the fundamental issue of charging path DEsign with the Minimized energy cOst (DEMO), i.e., given a set of rechargeable sensors, we appropriately design the MC’s charging path to minimize the energy cost which is due to the wireless charging and the MC’s movement, such that the different charging demand of each sensor is satisfied. Solving DEMO is NP-hard and involves handling the tradeoff between the charging efficiency and the moving cost. To address DEMO, we first develop a computational geometry-based algorithm to deploy multiple charging positions where the MC stays to charge nearby sensors. We prove that the designed algorithm has the approximation ratio of O(lnN), where N is the number of sensors. Then we construct the charging path by calculating the shortest Hamiltonian cycle passing through all the deployed charging positions within the network. Extensive evaluations validate the effectiveness of our path design in terms of the MC’s energy cost minimization.

The knowledge-enhanced BERT model solves the problem of lacking knowledge in downstream tasks by injecting external expertize, and achieves higher accuracy compared with BERT model. However, owning to large-scale external knowledge is utilized into knowledge-enhanced BERT, some shortcomings comes such as information noise, lower accuracy and weak generalization ability, and so on. To solve this problem, a knowledge enhancement method BERTBooster which combines incremental learning and gradient optimization is proposed. BERTBooster disassembles the input text corpus into entity noun sets through entity noun recognition, and uses the incremental learning task denoising entity auto-encoder to create an incremental task set of entity nouns and external knowledge triples. Furthermore, BERTBooster introduces a new gradient optimization algorithm ChildTuningF into BERT model to improve the generalization ability. BERTBooster can effectively improve the factual knowledge cognition ability of CAGBERT model and improve the accuracy of the model in downstream tasks. Experiments are carried out on six public data sets such as Book_Review, LCQMC, XNLI, Law_QA, Insureace_QA, and NLPCC-DBQA. The experimental results show that the accuracy rate in downstream tasks is increased by 0.65% on average after using BERTBooster on CAGBERT.

Terahertz (THz) wave is an electromagnetic wave with a frequency between far infrared ray and millimeter wave, which is widely used in hazardous material detection for its waveband fingerprint spectroscopy. THz time-domain spectroscopy technology based on deep learning can be used for nondestructive detection of various hazardous materials by recognizing the fingerprint spectrum of substances. However, due to the high cost of collecting spectral data, training samples are not easy to obtain and scarce for classification models, which leads to poor training effectiveness and low accuracy of classification. To address this problem, a fully connected layer-based auxiliary classifier generative adversarial network (FC-ACGAN) data augmentation method is proposed in this paper, we realized the generator and discriminator with fully connected layers to fit original data distribution better and generate data with higher quality. First, THz time-domain spectral data from seven flammable liquids were augmented using Mixup and FC-ACGAN, and then we fed the generated data set and expanded data set into Residual Network (ResNet), convolutional neural network, fully convolutional network, and multilayer perceptron for training. It is demonstrated that our method can solve the overfitting of models because of insufficient data. Compared with direct training on original data set, the accuracy of models using augmented data set improved by 5.1325% on average, which is 3.15% higher than that using Mixup. Furthermore, we experimented on expanded data set with ResNet long short-term memory for classification, the final accuracy reaches 99.42% on average, which is 1.09% higher than that using the original data set.

Unmanned Aerial Vehicle (UAV) is a promising technology that can serve as aerial base stations to assist Internet of Things (IoT) networks, solving various problems such as extending network coverage, enhancing network performance, transferring energy to IoT devices (IoTDs), and perform computationally-intensive tasks of IoTDs. Heterogeneous IoTDs connected to IoT networks have limited processing capability, so they cannot perform resource-intensive activities for extended periods. Additionally, IoT network is vulnerable to security threats and natural calamities, limiting the execution of real-time applications. Although there have been many attempts to solve resource scarcity through computational offloading with Energy Harvesting (EH), the emergency and vulnerability issues have still been under-explored so far. This paper proposes a blockchain and multi-agent deep reinforcement learning (MADRL) integrated framework for computation offloading with EH in a multi-UAV-assisted IoT network, where IoTDs obtain computing and energy resources from UAVs. We first formulate the optimization problem as the joint optimization problem of computation offloading and EH problems while considering the optimal resource price. And then, we model the optimization problem as a Stackelberg game to investigate the interaction between IoTDs and UAVs by allowing them to continuously adjust their resource demands and pricing strategies. In particular, the formulated problem can be addressed indirectly by a stochastic game model to minimize computation costs for IoTDs while maximizing the utility of UAVs. The MADRL algorithm solves the defined problem due to its dynamic and large-dimensional properties. Finally, extensive simulation results demonstrate the superiority of our proposed framework compared to the state-of-the-art.

Critical energy infrastructure (CEI) systems are vital to underpin the national economy and social development, but vulnerable to cyber attack and data privacy leakage when distributed machine learning technologies are deployed on them. Although federated learning (FL) has promoted distributed collaborative learning while keeping natural compliance with the privacy protection, it is tremendously difficult to schedule edge nodes of CEI collaboratively when asynchronous FL tasks are applied in CEI system, since the CEI system must make an irrevocable immediate decision on whether to hire a participant who arrives and departs dynamically without knowing future information. In this article, we tackle this issue by designing fairness-aware and time-sensitive task allocation mechanisms in asynchronous FL for CEI. First, we design an optimal multidimensional contract to guarantee the reliability, honesty, and fairness, and maximize the learning accuracy for the fixed deadline scenario. Second, we design a multimetric participant recruitment mechanism to control time consumption for the limited budget scenario, prove that the problem of optimizing this mechanism is NP-hard, and propose an$e$-approximation algorithm accordingly. Finally, extensive experiments using both real-world data and simulated data further demonstrate the effectiveness and efficiency of our proposed mechanisms compared to the state-of-the-art approaches.

Although mobile crowdsensing (MCS) has become a green paradigm of collecting, analyzing, and exploiting massive amounts of sensory data, existing incentive mechanisms are not effective to stimulate users’s active participation and service contribution in multi-service exchange in MCS due to its specific features: a large number of heterogeneous users have asymmetric service requirements, workers have the freedom to choose sensing tasks as well as participation levels, and multiple sensing tasks have heterogeneous values which may be untruthful declared by the corresponding requesters. To address this issue, this article develops a green Stackelberg-game incentive mechanism to achieve selective fairness, truthfulness, and bounded efficiency while reducing the burden on the platform. First, we model the multi-service exchange problem as a Stackelberg multi-service exchange game consisting of multi-leader and multi-follower, in which each requester as a leader first chooses the reward declaration strategy and thus the payment for each sensing task, each worker as a follower then chooses the sensing plan strategy to maximize her own utility. We next introduce the concept of virtual currency to maintain the selective fairness to balance service request and service provision between users, in which a user earns/consumes virtual currency for providing/receiving services, and thus no one can always get services without providing services. Then, we present two novel algorithms to compute the unique Nash equilibrium for the sensing plan determination game and the reward declaration determination game, respectively, which together forms a unique Stackelberg equilibrium for the proposed game. Afterwards, we theoretically prove that the proposed green Stackelberg-game incentive mechanism achieves the desirable properties of selective fairness, truthfulness, bounded efficiency. Finally, extensive evaluation results are provided to support the validity and effectiveness of our mechanism compared with both baseline and theoretical optimal approaches.

Wireless power transfer (WPT) enables a reliable and convenient charging paradigm. This article concerns the fundamental issue of energy saving in wireless rechargeable sensor networks (WRSNs), i.e., given a fixed number of rechargeable sensors (RSs) with their locations and charging demands, we focus on a minimal charging expenditure (MAP) problem with directional WPT to decrease the energy expenditure of the charger, on condition that the charging demands of all sensors are satisfied. In particular, we consider the anisotropic energy receiving property of RSs, which is closely related to the distance and the angle between the sensor and the charger antenna's orientation in directional WPT. We transform the MAP problem into an optimal function placement (OFA) problem, which can be geometrically analyzed in a rectangular coordinate system and is NP-hard. First, we study the OFA problem in the case of uniformly distributed sensors with identical charging demands, we develop the uniform charging strategy (UCS) to bound the total charging expenditure as Θ(1) for any number of sensors N. Based on the acquired insights, we further studied the OFA problem when the distribution of charging demands is Gaussian. We bound the total charging expenditure as Θ(1) for any number of sensors N, by developing the layered charging strategy (LCS). Extensive simulation results confirmed the performance of our design compared with two baseline algorithms. Both of the theoretical and simulations results reveal that the total energy expenditure of the charger is strongly related to the sensors' charging demands, however, is less affected by the number of sensors in the network.

This article reports on the development of a deep Q-learning approach to long-term energy collection in self-sustainable sensor networks, which consists of two static chargers (SCs) and one rechargeable mobile sensor (MS). In particular, we assume that the SCs can harvest energy from the ambient environment and charge the MS via electromagnetic (EM) radiation. As the energy harvesting (EH) process is random and the radiated energy fades over distance, the achievable energy by the MS at each slot demonstrates spatiotemporal dynamics in a certain area. Thus, we focus on the problem of trajectory optimization for an autonomous MS to maximize the long-term average achievable energy per slot from both chargers. Due to the inaccessible charger-side information, such as the EH profile and locations of SCs as well as the transmit power, we introduce deep Q-learning, a model-free reinforcement learning approach, based on which the MS can learn and optimize the moving trajectory by intelligently tracking the aggregated received EM signal without any other explicit external information. Simulation results show that the MS can identify the best energy collecting location and finally moves there along the learned trajectory. We also investigate the impact of system parameters, such as initial position and moving cost per unit distance on the performance of the proposed training algorithm, such as convergence rate and stability via extensive numerical evaluations.

Although crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Existing incentive mechanisms are not effective to avoid socially undesirable equilibrium due to the following features of competitive crowdsourcing: in the presence of imperfect monitoring, heterogeneous workers with competing interest tend to beat their opponents for larger self-profit, and the fact that they can freely and frequently change their opponents makes the situation much more complicated. Taking these features into consideration, this article proposes a mechanism design problem to enforce cooperation and extort selfish works simultaneously, with the objective of maximizing the requester's utility. To solve the problem, we integrate binary ratings with differential pricing to develop a novel rating protocol. By establishing a mathematical model for the problem and quantifying necessary and sufficient conditions for a sustainable social norm, we provide design guidelines for optimal rating protocols and design a low-complexity algorithm to select optimal design parameters. Finally, extensive evaluation results demonstrate the performance of our proposed rating protocol and reveal how intrinsic parameters impact on design parameters.

Despite the increasing popularity and successful examples of crowdsourcing, it is stripped of aureole when collective efforts are derailed or severely hindered by elaborate sabotage. A service exchange dilemma arises when there is non-cooperation among self-interested users, and zero social welfare is obtained at myopic equilibrium. Traditional rating protocols are not effective to overcome the inefficiency of the socially undesirable equilibrium due to specific features of crowdsourcing: a large number of anonymous users having asymmetric service requirements, different service capabilities, and dynamically joining/leaving a crowdsourcing platform with imperfect monitoring. In this paper, we develop the first game-theoretic design of the two-sided rating protocol to stimulate cooperation among self-interested users, which consists of a recommended strategy and a rating update rule. The recommended strategy recommends a desirable behavior from three predefined plans according to intrinsic parameters, while the rating update rule involves the update of ratings of both users, and uses differential punishments that punish users with different ratings differently. By quantifying necessary and sufficient conditions for a sustainable social norm, we formulate the problem of designing an optimal two-sided rating protocol that maximizes the social welfare among all sustainable protocols, provide design guidelines for optimal two-sided rating protocols and a low-complexity algorithm to select optimal design parameters in an alternate manner. Finally, evaluation results show the validity and effectiveness of our protocol designed for service exchange dilemma in crowdsourcing.

Despite the increasing popularity and the perceived promise of crowdsourcing, its openness presents individuals with an opportunity to exhibit antisocial behavior, such as free-ride and attack to decrease the social welfare, which is considered as a crowdsourcing contest dilemma. Hence, incentive mechanisms are needed to compel rational and selfish individuals to contribute well behavior in tasks. In this paper, we integrate the pricing and reputation schemes to design a novel socially optimal rating protocol based on game theory, in which each player is tagged with a rating to represent its social status, and players are encouraged to contribute good behaviors to increase their ratings, thus receive higher rewards. In particular, we analyze how the players' behaviors are influenced by the incurred costs and the designed payment, as well as their long-term utilities. By quantifying the sufficient and necessary conditions under which all players comply with the social norm in their self-interests, we formulate the rating protocol design problem, and analyze the impacts of the design parameters in order to characterize the optimal design, that maximizes the social welfare to achieve the social optimum. Finally, illustrative results show the validity and effectiveness of our proposed protocol design for crowdsourcing contest dilemma.

As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant ads into a Web page, so as to increase the number of ad-clicks. However, some problems of homonymy and polysemy, low intersection of keywords etc., can lead to the selection of irrelevant ads for a page. In this paper, we present a new contextual advertising approach to overcome the problems, which uses Wikipedia concept and category information to enrich the content representation of an ad (or a page). First, we map each ad and page into a keyword vector, a concept vector and a category vector. Next, we select the relevant ads for a given page based on a similarity metric that combines the above three feature vectors together. Last, we evaluate our approach by using real ads, pages, as well as a great number of concepts and categories of Wikipedia. Experimental results show that our approach can improve the precision of ads-selection effectively.

Abstract The principle of least privilege in role‐based access control is an important area of research. There are two crucial issues related to it: the specification and the enforcement. We believe that the existing least privilege specification schemes are not comprehensive enough and few of the enforcement methods are likely to scale well. In this paper, we formally define the basic principle of least privilege problem and present different variations, called the delta‐approx principle of least privilege problem and the minimizing‐approx principle of least privilege problem. Since there may be more than one result to enforce the same principle of least privilege, we introduce the notation about weights of permissions and roles to optimize the results. Then we prove that all least privilege problems are NP‐complete. As an important contribution of the paper, we show that the principle of least privilege problem can be reduced to minimal cost set covering (MCSC) problem. We can borrow the existing solutions of MCSC to solve the principle of least privilege problems. Finally, different algorithms are designed to solve the proposed least privilege problems. Experiments on performance study prove the superiority of our algorithms. Copyright © 2011 John Wiley & Sons, Ltd.

Traditional web application development often encounters tight coupling problem between access control logic and business logic. It is hard to configure and modify access control policies after a system has been deployed. In this demonstration, we present SecTag, a multi-policy supported secure web tag framework, to address this problem. We define a series of general-purpose secure attributes that meet the demand of fine-grained access control in web presentation layer. We also design a set of high interactive secure tags, which encapsulate secure features to provide reusable secure components for web development. A running example of SecTag is presented to demonstrate the effectiveness of the proposed framework.