Along with the advance of low-altitude economy, a variety of applications based on Unmanned Aerial Vehicles (UAVs) have been developed to accomplish diverse tasks. In this paper, we focus on the scenario of multiple UAVs performing Federated Learning (FL) tasks. Specifically, a group of UAVs is scheduled to repeatedly visit some Points of Interest (PoIs), collect the data produced by these PoIs, and jointly train a machine learning model based on the collected data. The most challenging issue is how to schedule UAVs to collect data so as to optimize the generalization and convergence of model training under the case that the distributions of the data produced by PoIs have not been known in advance. To address this issue, we propose a novel framework for online FL on distributed unknown data, named OFL-UD2, which is dedicated to online decision-making for UAVs to optimize model training performance. Concretely, we formulate the optimization problem while considering the convergence and quality of trained models as well as energy constraints. Then, we define a utility metric for the data quality of different PoIs and conduct a rigorous convergence analysis for OFL-UD2. Based on the analysis results, we design a two-stage algorithm to determine the scheduling of UAVs. Extensive simulations demonstrate that OFL-UD2can improve model accuracy and speed up running time compared to existing benchmarks significantly.

Federated learning (FL) is a distributed learning paradigm that enables large-scale IoT devices to collaboratively train a shared model while preserving the privacy of local data. To avoid the single-point-of-failure of the conventional parameter server architecture, the study concentrates on the decentralized FL (DFL) paradigm building on the device-to-device communication network. However, existing DFL frameworks encounter challenges related to resource limitations, privacy protection, and data heterogeneity. To overcome these challenges, the study proposes and implements DF$^{2}$-MPC in industrial IoT, an efficient DFL framework with personalized model pruning and adaptive communication. Specifically, a personalized pruning ratio determination approach is designed by exploiting the model pruning technique. This approach enables all devices to flexibly determine pruning ratios by themselves, thereby achieving both communication savings and privacy protection. Then, this study designs an adaptive neighbor selection scheme, which can enhance model performance and foster model consensus under resource constraints. In addition, the study theoretically proves the convergence performance of DF$^{2}$-MPC. Finally, extensive simulations on three real-world traces are conducted to corroborate the superiority of DF$^{2}$-MPC, demonstrating that the method can improve communication efficiency with satisfactory model accuracy and convergence performance.

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines.

Federated Learning (FL) is an emerging privacy-preserving distributed machine learning paradigm that enables numerous clients to collaboratively train a global model without transmitting private datasets to the FL server. Unlike most existing research, this paper introduces a Data-Driven FL system in Unmanned Aerial Vehicle (UAV) networks, named DDFL, which features an innovative three-layer architecture. In this architecture, UAVs, inherently lacking data, serve as mobile clients and are tasked with periodically collecting desired data from a group of Points of Interest (PoIs) for FL training. Meanwhile, a Base Station (BS) coordinates these UAVs to efficiently train a high-quality global model. Our objective is to determine a data collection strategy for each UAV to minimize the time required to meet the global model’s loss requirement within the constraint of energy consumption. Through theoretical analysis, we establish a bound for the convergence speed of DDFL and quantify the impact of collecting data from different PoIs on the global model’s loss function. Leveraging these analyses, we formulate the PoI selection problem as a novel two-stage Combinatorial Multi-Armed Bandit (CMAB) problem with multiple constraints. We then propose an Adaptive Two-stage CMAB-based algorithm, named FedATC, to jointly optimize the data collection route and UAV velocity. Extensive simulations demonstrate that FedATC significantly reduces the time required to achieve desired model quality compared to state-of-the-art algorithms.

In recent years, Crowdsensing Data Trading (CDT) has emerged as a new data trading paradigm, where buyers crowdsource data collection tasks to a group of mobile users with sensing devices (a.k.a., sellers) who sell the collected data to them, through a platform as the broker for a long-term data trading. One of the most critical issues in CDT is ensuring the stability of the matching between buyers and sellers in the data trading market. In this paper, we focus on privacy protection and the stability problems in the CDT market with unknown preference sequences of buyers. The goal is to protect sellers' data qualities and ensure the CDT market's stability while maximizing the cumulative data quality for each task. We model this problem as a differentially private multi-player multi-armed competing bandit problem and propose a novel metric of the approximate stability, called$\delta$-stability. We propose a privacy-preserving stable CDT mechanism called DPS-CB to solve this problem in the centralized setting, which is based on stable matching theory, and competing bandit strategy. Moreover, we extend it into decentralized setting in order to avoid the competitive matching conflicts caused in this setting and propose a Conflicts-avoiding DPS-CB mechanism, called CDPS-CB, by using Bernoulli probability and selecting feasible sets of sellers. In addition, we prove the security and stability of the CDT market under privacy concerns and analyze the regret performance of DPS-CB and CDPS-CB mechanisms, respectively. Finally, the significant performance of these two mechanisms is demonstrated through extensive simulations on a real-world dataset.

In this paper, we focus on the privacy preserving mechanism design for crowdsourced Federated Learning (FL), where a requester can outsource its model training task to some workers via an FL platform. A potential way to preserve the privacy of workers' local data is to leverage Differential Privacy (DP) mechanisms on local models. However, most of these studies cannot allow workers to dominate their own privacy protection levels by themselves. Thus, we propose a Personalized Privacy Preserving Mechanism, called P3M, to satisfy the heterogeneous privacy needs of workers, which consists of two parts. The first part includes a personalized privacy budget determination problem. We model it as a two-stage Stackelberg game, derive the personalized privacy budget for each worker and the optimal payment for the requester, and prove that they form a unique Stackelberg equilibrium. Second, we design a dynamic perturbation scheme to perturb model parameters. Through the theoretical analysis, we prove that P3M satisfies the desired DP property, and derive the bounds of the variance of average perturbed parameters and the convergence upper bound. This demonstrates that the global model accuracy can be controllable and P3M is endowed with the satisfactory convergence performance. In addition, we extend our problem to the scenario where the total privacy budget of all workers is limited, so as to prevent some workers from setting exorbitant privacy budgets. Under the privacy constraint, we re-determine the personalized privacy budget for each worker. Finally, exhaustive simulations of P3M are conducted based on real-world datasets, and the experimental results corroborate its effectiveness and practicability.

With the explosive spread of smart mobile devices, Mobile CrowdSensing (MCS) has been becoming a promising paradigm, by which a platform can coordinate a group of workers to complete large-scale data collection tasks using their mobile devices. In this paper, we investigate the incentive mechanism design in MCS systems, taking the freshness of collected data and social benefits into consideration. First, the Age of Information (AoI) metric is introduced to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as an incomplete information two-stage Stackelberg game with multiple constraints. Next, we consider the scenario that all participants share the public utility function parameters of the Stackelberg game. By deriving the optimal remuneration paid by the platform and the optimal data update frequency for each worker, and proving the existence of a unique Stackelberg equilibrium, we propose an AoI-guaranteed Incentive Mechanism (AIM) that enables the platform and all workers to maximize their utilities simultaneously. Furthermore, we extend AIM to a general scenario where each participant has no prior knowledge of the utility function parameters of the game. By resorting to the Deep Reinforcement Learning (DRL) technique and modeling the two-stage Stackelberg game as a Markov decision process, we propose a DRL-based Incentive Mechanism (DIM) with AoI guarantees, which makes each participant effectively seek its optimal strategy through trial and error. Meanwhile, the system can guarantee that the AoI values of all data uploaded to the platform are not larger than a given threshold. Finally, numerical experiments on real-world traces are conducted to validate the efficacy and efficiency of AIM and DIM.

In this paper, we investigate the privacy-preserving task push problem with unknown popularity in Spatial Crowdsourcing (SC), where the platform needs to select some tasks with unknown popularity and push them to workers. Meanwhile, the preferences of workers and the popularity values of tasks might involve some sensitive information, which should be protected from disclosure. To address these concerns, we propose a Privacy Preserving Auction-based Bandit scheme, termed PPAB. Specifically, on the basis of the Combinatorial Multi-armed Bandit (CMAB) game, we first construct a Differentially Private Auction-based CMAB (DPA-CMAB) model. Under the DPA-CMAB model, we design a privacy-preserving arm-pulling policy based on Diffie-Hellman (DH), Differential Privacy (DP), and upper confidence bound, which includes the DH-based encryption mechanism and the hybrid DP-based protection mechanism. The policy not only can learn the popularity of tasks and make online task push decisions, but also can protect the popularity as well as workers’ preferences from being revealed. Meanwhile, we design an auction-based incentive mechanism to determine the payment for each selected task. Furthermore, we conduct an in-depth analysis of the security and online performance of PPAB, and prove that PPAB satisfies some desired properties (i.e., truthfulness, individual rationality, and computational efficiency). Finally, the significant performance of PPAB is confirmed through extensive simulations on the real-world dataset.

Mobile CrowdSensing (MCS) is a mobile computing paradigm, through which a platform can coordinate a crowd of workers to accomplish large-scale data collection tasks using their mobile devices. Information freshness has attracted much focus on MCS research worldwide. In this paper, we investigate the incentive mechanism design in MCS systems that take the freshness of collected data and social benefits into concerns. First, we introduce the Age of Information (AoI) metric to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as a novel incomplete information two-stage Stackelberg game with multiple constraints. Next, we derive the optimal strategies of this game so as to determine the optimal reward paid by the platform and the optimal data update frequency for each worker. Moreover, we prove that these optimal strategies form a unique Stackelberg equilibrium. Based on the optimal strategies, we propose an AoI-Aware Incentive (AIAI) mechanism for the MCS system, whereby the platform and all workers can maximize their utilities simultaneously. Meanwhile, the system can ensure that the AoI values of all data uploaded to the platform are not larger than a given threshold to achieve high data freshness. Extensive simulations on real-world traces are conducted to demonstrate the significant performance of AIAI.

In this paper, we investigate the incentive problem in Spatial Crowdsourcing (SC), where mobile social-aware workers have unknown qualities and can share their answers to tasks via social networks. The objectives are to recruit high-quality workers and maximize all parties’ utilities simultaneously. However, most existing works assume that the qualities of workers are known in advance or cannot take all parties’ utilities into account together, especially having not considered the impact of social networks. Thus, we propose an incentive mechanism based on the multi-armed bandit and three-stage Stackelberg game, called TACT. We first design a greedy arm-pulling scheme to recruit workers, which not only can solve the exploration-exploitation dilemma but also takes workers’ social relations into account. Based on the recruitment results, we further design the utility functions incorporating with social benefits for workers, and model the payment computation problem as a three-stage Stackelberg game among all participants. Next, we derive the optimal strategy group so that each party can maximize its own utility to form a multi-win situation. Moreover, we theoretically prove the unique existence of Stackelberg equilibrium and the worst regret bound. Finally, we conduct extensive simulations on a real trace to corroborate the performance of TACT.

Nowadays, crowdsourcing has become an increasingly popular paradigm for large-scale data annotation. It is crucial to ensure label quality by selecting the most suitable workers for labeling tasks. Many previous works have studied the reliability of unknown workers for crowdsourcing tasks with a stochastic assumption. However, each worker's reliability varies when performing tasks with different categories. Meanwhile, the reliability of each worker is usually unknown and doesn't follow any stochastic distribution. In this paper, we propose an Adversarial multi-armed Bandit-base algorithm to handle the Unknown Worker Recruitment (ABUWR) problem without any prior stochastic assumption. In ABUWR, we determine suitable workers for each task to maximize the accumulated average accuracy of the labeling tasks under a limited budget. Specifically,$w$e model this unknown worker recruitment problem as an adversarial multi-armed bandit game and use the least confidence scheme to ensure the total accumulate accuracy. Meanwhile,$w$e theoretically prove that ABUWR has a sub-linear regret upper bound. Furthermore, we demonstrate its significant performance through extensive simulations on real-world data traces.

Fog computing is a potential technology that can solve computationally intensive, latency-sensitive, and energy-intensive tasks. This paper studies the task offloading problem for decentralized multi-users with unknown system-side information. On the basis, we design the distributed task offloading bandit (DTOB) algorithm to balance task offloading exploration and exploitation in fog computing (MFC) systems. Finally, the effectiveness of the offloading scheme based on bidirectional matching is verified by experiments.

Traditional spatial crowdsourcing (SC) systems employ a centralized server platform to provide services for requesters. Such a centralized design requires powerful resource capacity and often cannot accomplish the urgent demands due to the unpredictable network latency. In order to ensure the scalability of systems and the quality of services, we study the distributed SC (DSC), where a diversity of location-relative services provided by various service providers (SPs) can deploy on edge clouds (ECs) with low time latency. Since the edge resources are limited, SPs need to compete for edge resources so as to deploy their desired SC services, and the requested resources must be allocated together to meet the demand of the service. We first design a gated recurrent unit with particle filter (GRUPF) network for SPs to predict future resource demands so as to participate in the competitions judiciously. Then, we model the competitive edge resource allocation problem between SPs and ECs as a combinatorial auction process. Due to the NP-hardness of this problem, an approximation algorithm is proposed to tackle it. Moreover, the leakage of private information such as bids may incur severe economic damage, and most existing studies usually rely on a trusted third party to provide rigorous privacy protection. Therefore, we customize a novel differentially private resource auction (DRA) mechanism, and design a bid confusion strategy based on differential privacy. Through theoretical analysis, we prove that the DRA mechanism meets some desired properties, including$\epsilon $-differential privacy, individual rationality, computational efficiency, and$\gamma $-truthfulness. Additionally, we corroborate the significant performances of DRA through extensive simulations on synthetic and real-world data sets.

Federated learning (FL) is a newly emerging distributed machine learning paradigm, whereby a server can coordinate multiple clients to jointly train a learning model by using their private datasets. Many researches focus on designing incentive mechanisms in FL, but most of them cannot allow that clients flexibly determine privacy budgets by themselves. In this article, we propose a privacy-preserving incentive mechanism (NICE) based on differential privacy (DP) and Stackelberg game for FL systems in industrial Internet of Things. First, we design a flexible privacy-preserving mechanism for NICE, in which clients can add a Laplace noise into the loss function according to a customized privacy budget. Under this mechanism, we design two incentive utility functions for the server and clients. Next, we model the utility optimization problems as a two-stage Stackelberg game by seeing the server as a leader and the clients as followers. Finally, we derive an optimal Stackelberg equilibrium solution for both the stages of the whole game. Based on this solution, NICE can make the server and all clients achieve their maximum utilities simultaneously. In addition, we conduct extensive simulations on real-world datasets to demonstrate the significant performance of the proposed mechanism.

present, edge cloud is becoming more and more important as it provides computing resources required by mobile terminals. The online edge cloud resource trading mechanism has become one of the most important ways for edge cloud resource sharing, attracting much attention. In this paper, we have proposed a Blockchain-based Privacy-preserving Combinatorial Double Auction (BPCDA) mechanism, which combines trustworthiness, truthfulness and privacy protection for the first time. Firstly, BPCDA avoids dependence on third-party brokers by adopting blockchain. Secondly, we draw on the economic theories, and then adopt double auction to ensure the truthfulness of bids while participants in the auction are composed of multiple sellers and multiple buyers. Lastly, we make fell use of the differential privacy mechanism on the blockchain to avoid privacy leakage. Through simulated experiments, we verify the superior performance and practicality of our proposed edge cloud resource trading mechanism.

Federated Learning, through which a server can coordinate a crowd of clients to accomplish a machine learning task, has been recognized as a promising paradigm for privacy preserving decentralized learning in recent years. Most federated learning researches are on the basis of IID data, and more and more researches focus on the Non-IID data problem. However, they have not considered the process of data acquisition. In fact, in real applications, the training data are typically collected by clients in real scenarios. In this paper, we propose a Federated Learning framework based on Dynamic Client Selection, called FedDCS, to deal with the real scene Non-IID data machine learning problem in federated learning. The objective of FedDCS is to utilize a parameter estimation algorithm to select the optimal clients to join the collaboration and finally acquire a better global machine learning model. Our extensive experiments on several real-world data sets demonstrate the superior performance of FedDCS.