Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their local data. However, the distributed nature of FL makes it vulnerable to backdoor attacks from malicious clients. Most existing attack methods often assume that attackers can inject backdoors in every training round - a scenario that is both unrealistic and inefficient in real-world FL deployment. In this paper, we investigate why backdoor attacks become less effective under low-frequency injection and propose a novel attack paradigm for FL, called REinforced Memorization-based INterval backDoor attack (REMIND). REMIND optimizes the backdoor trigger via task alignment and feature alignment. Task alignment aligns backdoor and main task objectives to resist benign update suppression during non-attack rounds, while feature alignment guides poisoned samples to match the activation trajectory of target-class samples. This dual alignment enhances the backdoor's persistence and narrows the divergence between malicious and benign updates. With strong attack success rates established, we further analyze the advantages of low-frequency backdoor attacks, particularly their ability to improve robustness against defense mechanisms. Extensive evaluations on four benchmark datasets show that REMIND consistently outperforms eight state-of-the-art attack baselines under nine defense strategies.

Vertical federated learning (VFL) enables a privacy-preserving collaboration among various parties to train a global model by melding their geo-distributed data features. Communication has been recognized as the primary bottleneck that impairs training efficiency due to frequent cross-party statistics exchange over wide area network. Existing synchronous VFL works often suffer from excessive communication overhead, while asynchronous schemes may introduce significant model staleness, potentially eroding the learning accuracy. In this paper, we propose BS-VFL, an asynchronous VFL with bounded staleness, to pipeline local computation and statistics transmission, substantially reducing the communication overhead while ensuring favorable model performance. Specifically, all data parties will give precedence to local model updates before generating embeddings to curtail model staleness. By analyzing convergence error, we show that BS-VFL can achieve a comparable result to synchronous VFL. Then, we develop a general framework to derive the closed-form wall-clock time of BS-VFL, offering a measure of its runtime efficiency and highlighting a marked communication reduction. Utilizing this convergence and time analysis, we refine learning parameters to minimize the convergence error for optimizing BS-VFL performance without compromising training efficiency. Extensive experiments on real-world datasets validate the superiority of BS-VFL over leading-edge methods, evidencing a reduction in training duration by 48%-90% while preserving model accuracy.

Private inference outsourcing ensures the privacy of both clients and model owners when model owners deliver inference services to clients through third-party cloud servers. Existing solutions either reduce inference accuracy due to model approximations or rely on the unrealistic assumption of non-colluding servers. Moreover, their efficiency falls short of HELiKs, a solution focused solely on client privacy protection. In this paper, we propose Skybolt, a single-server private inference outsourcing framework without resorting to model approximations, achieving greater efficiency than HELiKs. Skybolt is built upon efficient secure two-party computation protocols that safeguard the privacy of both clients and model owners. For the linear calculation protocol, we devise a ciphertext packing algorithm for homomorphic matrix multiplication, effectively reducing both computational and communication overheads. Additionally, our nonlinear calculation protocol features a lightweight online phase, involving only the addition and multiplication on secret shares. This stands in contrast to existing protocols, which entail resource-intensive techniques such as oblivious transfer. Extensive experiments on popular models, including ResNet50 and DenseNet121, show that Skybolt achieves a 5.4 − 7.3× reduction in inference latency, accompanied by a 20.1 − 39.6× decrease in communication cost compared to HELiKs.

Federated Learning (FL) aims to train neural network models using distributed data resources from multiple clients without sharing raw data. One of the key challenges in FL is non-independent and identically distributed (non-IID) data, which may affect model accuracy. To address this issue, some schemes leverage Generative Adversarial Networks (GANs) to generate virtual data and combine it with the real data to achieve a balanced data distribution. However, there are risks of privacy leakage from the collected virtual data and aggregated gradients. In this paper, we propose GetFed, an accurate and differentially private FL framework with GAN-based Data Generation on non-IID Data. We integrate Differential Privacy (DP) into the GAN training and federated aggregation phases to prevent clients’ privacy leakage. To balance privacy and accuracy, we first design a privacy-preserving virtual sample generation algorithm for GAN training that dynamically reduces unnecessary noise as the quality of virtual samples improves. Additionally, we design an adaptive DP-based secure aggregation algorithm that decreases the added noise as the model approaches convergence. Furthermore, we implement a real-virtual ensemble training algorithm, employing an ensemble learning strategy to better mix virtual and real samples for enhanced global model accuracy. This approach ensures clients benefit from both the authenticity of real samples and the balanced data distribution provided by virtual samples, effectively mitigating the data heterogeneity inherent in non-IID scenarios. Extensive experiments demonstrate that compared with state-of-the-art schemes, GetFedimproves model accuracy by 6–47% and reduces training time by 50%.

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads,packedHE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted byhonest-but-curious clients. Also, it does not consider theheterogeneityamong different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provideobscuredencrypted updates while the server can perform aggregation by accounting forcontributionsof local updates. To mitigate the straggler effect, we devise aperturbed sketch-based selection to cherry-pick representative clients withheterogeneous models and computing capabilitiesin a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve$2.45-6.56\times$training speedup, cut the communication overhead by$1.32-24.85\times$, and reduce straggler effects by$1.89-2.78\times$.

Existing backdoor defense methods for federated learning (FL) usually try to distinguish between benign and malicious clients. The key insight is that benign clients are densely distributed, whereas malicious clients tend to be outliers outside this distribution. However, this only holds when data is independent and identically distributed (IID), and the effectiveness of these methods under non-IID data has not been systematically examined. In this paper, we present a comprehensive systematization of FL backdoor defense by breaking down its overall pipeline into three key components, i.e., metrics for evaluating clients, techniques for amplifying the difference between benign and malicious clients, and mechanisms for identifying malicious clients. We conduct an empirical study of FL backdoor defense methods under non-IID data settings to explore whether benign and malicious clients can be fully distinguished. Experimental results show that the defense performance degrades significantly when data is non-IID. Our results also reveal how evaluation metrics, amplification techniques and identification mechanisms perform under diverse settings. Contrary to the established belief, we further conclude that these defenses have inherent shortcomings, due to lack of stability and robustness in detecting malicious clients. We believe that our findings can better facilitate the development of FL backdoor defenses.

Personalized Federated Learning (pFL) can collaboratively personalize models for multiple clients without sharing their private data. However, many pFL methods rely on server-side model parameters aggregation, which requires all models to have the same structure and size. One promising approach is leveraging knowledge distillation (KD) to transfer knowledge between models by exchanging soft predictions rather than model parameters, thus training heterogeneous models. Nevertheless, existing KD-based pFL solutions suffer from accuracy loss due to inadequate knowledge extraction as well as huge computing and communication overheads. In this paper, we present an accurate and efficient KD-based pFL framework, called ACE-pFL. Specifically, we first propose a privacy-preserving client clustering to reduce the impact of non-independent and identically distributed (non-IID) data on model accuracy and convergence, grouping clients with similar data distributions into the same cluster. Since the distillation temperature of traditional KD is fixed, which does not consider the dynamic model training process, we design a dynamic distillation temperature adjustment to accommodate this process, where clients incrementally increase the distillation temperature as training proceeds to facilitate model generalization to new data. Finally, we employ the triple distillation strategy to provide diverse and abundant knowledge, including explicit global knowledge, implicit local knowledge, and implicit global knowledge. Experiments on multiple datasets and tasks show that compared with existing schemes, ACE-pFL can significantly improve the test accuracy by 17.18%, reduce the training time by 57% and the communication overhead by$59.12\times $on average.

Pipeline parallelism has emerged as an indispensable technique for training large deep neural networks. While existing asynchronous pipeline systems address the time bubbles inherent in synchronous architectures, they continue to suffer frominefficiencyandsusceptibilitytovolatilehardware environment due to their suboptimal andstaticconfigurations. In this paper, we propose DynPipe, aninterference-awareasynchronous pipeline framework to optimize theend-to-endtraining performance in highlydynamiccomputing environments. By characterizing thenon-overlappedcommunication overheads andconvergencerate conditioned on stage-wise staleness, DynPipe carefully crafts an optimized pipeline partition that harmonizes the hardware speed with statistical convergence. Moreover, DynPipe deploys anon-intrusiverandom forest model that utilizes runtime stage statistics to evaluate the impact of environmental changes, such as task interference and network jitter, on the training efficiency. Following the evaluation guidance, DynPipe adaptivelyadjustspartition plan to restore both intra and inter-stage load balancing, thereby facilitating seamless pipeline reconfiguration in dynamic environments. Extensive experiments show that DynPipe outperforms state-of-the-art systems, accelerating the time-to-accuracy by1.5-3.4×.

Blockchain-based identity brings in great evolution due to its decentralized deployment, transparent and tamper-free ledger. Specification groups of B5G/6G are exploring into integrate the technology to future network systems, e.g., Internet of Things, vehicular network, industrial communications. However, devices in these systems often have storage constraints and unstable channels, which necessitates lightweight node deployment. The security issue arises: revoked identity can forge a legitimate authentication, since the lightweight verifier does not maintain the revocation transactions. This paper hence proposes AcBF, a novel revocable identity management scheme, that enables extremely low authentication latency by allowing the lightweight node to query the certificate's status locally. To realize this feature trustfully, we design a revocation transaction based on accumulator-assisted Bloom filter to minimize the storage of certificate status structure. Secondly, we construct the blockchain protocol to ensure that no revocation event slips on any lightweight ledger, even in an insecure or unstable communication environment. In addition, different from other revocation mechanisms, AcBF minimizes the impact on valid users during the revocation process. Through security and performance analysis, AcBF has shown strong security and advantageous efficiency on both lightweight verifiers and certificate owners, thus suits identity management systems with low-latency constraints.

Graph convolutional network (GCN) has achieved enormous success in learning structural information from unstructured data. As graphs become increasingly large, distributed training for GCNs is severely prolonged by frequent cross-worker communications. Existing efforts to improve the training efficiency often come at the expense of GCN performance, while the communication overhead persists. In this paper, we propose PSC-GCN, a holistic pipelined framework for distributed GCN training with communication-efficient sampling and inclusion-aware caching, to address the communication bottleneck while ensuring satisfactory model performance. Specifically, we devise an asynchronous pre-fetching scheme to retrieve stale statistics (features, embedding, gradient) of boundary nodes in advance, such that the embedding aggregation and model update are pipelined with statistics transmission. To alleviate communication volume and staleness effect, we introduce a variance-reduction based sampling policy, which prioritizes inner nodes over boundary ones for reducing the access frequency to remote neighbors, thus mitigating cross-worker statistics exchange. Complementing graph sampling, a feature caching module is co-designed to buffer hot nodes with high inclusion probability, ensuring that frequently sampled nodes will be available in local memory. Extensive evaluations on real-world datasets show the superiority of PSC-GCN over state-of-the-art methods, where we can reduce training time by 72%-80% without sacrificing model accuracy.

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing their private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE does not consider the heterogeneity among different clients, an intrinsic problem in cross-silo FL, often resulting in undermined training efficiency with slow convergence and stragglers. In this work, we propose FedPHE, an efficiently packed homomorphically encrypted FL framework with secure weighted aggregation and client selection to tackle the heterogeneity problem. Specifically, using CKKS with sparsification, FedPHE can achieve efficient encrypted weighted aggregation by accounting for contributions of local updates to the global model. To mitigate the straggler effect, we devise a sketching-based client selection scheme to cherry-pick representative clients with heterogeneous models and computing capabilities. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients’ privacy, achieve a training speedup of 1.85 − 4.44×, cut the communication overhead by 1.24 − 22.62× , and reduce the straggler effect by up to 1.71 − 2.39×.

Private Inference (PI) safeguards client and server privacy when the client utilizes the server’s model to make predictions. Existing PI solutions for Convolutional Neural Networks (CNNs) employ distinct cryptographic primitives to customize secure two-party protocols for linear and non-linear layers. This requires data to be converted into a specific form to switch between protocols, thus leading to a significant increase in inference latency. In this paper, we present Fregata, a fast PI scheme for CNNs by leveraging identical cryptographic primitives to calculate both linear and nonlinear layers. Specifically, our protocols utilize homomorphic encryption to obtain additive secret shares of matrix products during the offline phase, followed by lightweight multiplication and addition operations on these shares in the latency-sensitive online phase. Benefiting from uniformity, we accelerate inference from a holistic perspective by decoupling certain procedures of our protocols and executing them asynchronously. Moreover, to improve the efficiency of the offline phase, we elaborate a homomorphic matrix multiplication calculation method with reduced computation and communication complexity compared to existing approaches. Furthermore, we minimize inference latency by employing graphics processing units to parallelize the operations on the shares during the online phase. Experimental evaluations on popular CNN models such as SqueezeNet, ResNet, and DenseNet demonstrate that Fregata reduces 35-45 times inference latency over the state-of-the-art counterparts, accompanied by a 1.6-2.8 times decrease in communication overhead. In terms of total runtime, Fregata maintains a reduction of approximately 3 times.

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients' selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal O(T - 2/3) convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques. © 2023 IEEE.

Federated Learning (FL) is a secure distributed learning paradigm, which enables potentially a large number of devices to collaboratively train a global model based on their local dataset. FL exhibits two distinctive features in job requirement and client participation, where FL jobs may have different training criteria, and clients possess diverse device capabilities and data characteristics. In order to capture such heterogeneities, this paper proposes a new FL framework, Hca, which aims to strike a balance between the job completion time and model accuracy. Specifically, Hca builds upon a number of innovations in the following three phases: i) pre-estimation: we first derive the optimal set of parameters used in training in terms of the number of training rounds, the number of iterations and the number of participating clients in each round; ii) client selection: we design a novel device selection algorithm, which selects the most effective clients for participation based on both client historical contributions and data effectiveness; iii) model aggregation: we improve the classic FedAvg algorithm by integrating the model loss reduction in consecutive rounds as a weighted factor into aggregation computation. To evaluate the performance and effectiveness of Hca, we conduct theoretical analysis and testbed experiments over an FL platform FAVOR. Extensive results show that Hca can improve the job completion time by up to 34% and the model accuracy by up to 9.1%, and can reduce the number of communication rounds required in FL by up to 75% compared with two state-of-the-art FL frameworks.

Clock synchronization is essential in real-time applications of large-scale clusters. State-of-the-art Huygens clock synchronization reduces synchronization errors through offset probing loop correction between data center servers. However, Huygens does not offer a solution for large-scale clusters. In this paper, we propose a novel and scalable CAT-Sync clock synchronization system for large-scale clusters, which includes three key techniques: optimal probe topology Construction, probing channel Assignment, and Time-slice synchronization. In CAT-Sync, the workload of each host is the same and will not increase with the expansion of the cluster size. Our CAT-Sync system achieves a stable clock synchronization accuracy within 2 microseconds on 60 virtual machines, and the average clock offset for the entire synchronization process is improved by about 44.8% compared to Huygens.

Mobile edge computing enables resource-limited edge clouds (ECs) in federation to help each other with resource-hungry yet delay-sensitive service requests. Contrary to common practice, we acknowledge that mobile services are heterogeneous and the limited storage resources of ECs allow only a subset of services to be placed at the same time. This paper presents a jointly optimized design of cooperative placement and scheduling framework, named JCPS, that pursuessocial cost minimizationover time while ensuring diverse user demands. Our main contribution is a novel perspective on cost reduction by exploiting thespatial-temporal diversitiesinworkload and resource costamong federated ECs. To build a practical edge cloud federation system, we have to consider two major challenges:user deadline preferenceandECs’ strategic behaviors. We first formulate and solve the problem of spatially strategic optimization without deadline awareness, which is proved$\mathcal {NP}$-hard. By leveraging user deadline tolerance, we develop a Lyapunov-baseddeadline-drivenjoint cooperative mechanism under the scenario where the workload and resource information of ECs are known for one-shot global cost minimization. Theservice priorityimposed by deadline urgency drives time-critical placement and scheduling, which, combined with cooperative control, enables workloads migrated across different times and ECs. Given selfishness of individual ECs, we further design an auction-based cooperative mechanism to elicittruthful bidson workload and resource cost. Rigorous theoretical analysis and extensive simulations are performed, validating the efficiency of JCPS in realizing cost reduction and user satisfaction.

Mobile edge computing provides a platform facilitating individual servers to pool their resources locally for cooperative computation. One fundamental problem in this new paradigm is how to effectively allocate crowdsourced edge resources to users competing in a highly unpredicted environment. This, apparently, cannot be realized without a truthful open market. On the other hand, enforcing truthfulness potentially incurs privacy problems. There have been efforts in differentially private auctions, in which exponential mechanism, designed for single-sided single-item auctions, is a common solution. However, such an approach is not applicable in two-sided combinatorial edge markets, further complicated by the extra migration cost on energy-constrained users often imposed by online allocation. In this paper, we propose OPTA, an online privacy-preserving truthful double auction mechanism for dynamic resource cooperation at the edge. Given uncertainties in future market behaviors, we harness competitive analysis by decomposing the online optimization into a series of single-round auctions such that their objectives are iteratively adjusted to capture the temporally-coupled nature of the problem. In each round, by jointly considering the features of exponential mechanism and greedy heuristic, we design a near-optimal allocation policy with efficiency and privacy guarantee. We further implement a critical-value pricing scheme for winners, realizing the truthfulness in expectation. Building upon the single-round results, our overall online algorithm achieves a provable competitive ratio. We validate the desirable properties of OPTA through theoretical analysis and extensive simulations.

Cooperative offloading in mobile edge computing enables resource-constrained edge clouds to help each other with computation-intensive tasks. However, the power of such offloading could not be fully unleashed, unless trust risks in collaboration are properly managed. As tasks are outsourced and processed at the network edge, completion latency usually presents high variability that can harm the offered service levels. By jointly considering these two challenges, we propose OLCD, an Online Learning-aided Cooperative offloaDing mechanism under the scenario where computation offloading is organized based on accumulated social trust. Under co-provisioning of computation, transmission, and trust services, trust propagation is performed along the multi-hop offloading path such that tasks are allowed to be fulfilled by powerful edge clouds. We harness Lyapunov optimization to exploit the spatial-temporal optimality of long-term system cost minimization problem. By gap-preserving transformation, we decouple the series of bidirectional offloading problems so that it suffices to solve a separate decision problem for each edge cloud. The optimal offloading control can not materialize without complete latency knowledge. To adapt to latency variability, we resort to the delayed online learning technique to facilitate completion latency prediction under long-duration processing, which is fed as input to queued-based offloading control policy. Such predictive control is specially designed to minimize the loss due to prediction errors over time. We theoretically prove that OLCD guarantees close-to-optimal system performance even with inaccurate prediction, but its robustness is achieved at the expense of decreased stability. Trace-driven simulations demonstrate the efficiency of OLCD as well as its superiorities over prior related work.

Crowdsourcing is an effective paradigm in human centric computing for addressing problems by utilizing human computation power, especially in booming social Internet of Things (IoT). By leveraging mutual friendship between computing entities (i.e., workers), collaborative tasks can thus be routed and finally fulfilled by multihop friends with high expertise. However, crowdsourcing in social IoT may reveal the privacy of task requesters which results in a large dilemma. In this paper, we focus on designing a multihop routing incentive mechanism which can also preserve task requester's privacy. Specifically, a utility maximization problem under privacy and budget feasibility constraints is formulated. Defining the conditions for privacy insurance, we give guidelines on how many subtasks should an entire task be divided into, and analyze the tradeoff between privacy and task accuracy. To enable efficient crowdsourcing task routing in social IoT, we first consider 1-hop myopic routing case and propose a near-optimal task assignment algorithm with 1/2 approximation ratio for an arbitrary prior knowledge. We further design multihop payment policy to establish an equilibrium where workers are motivated to forward subtasks to their friends with the best expertise. The extensive simulations validate that our mechanism achieves a high level of average information gain with modest privacy guarantee.

Combined with many different attack forms, advanced persistent threats (APTs) are becoming a major threat to cyber security. Existing security protection works typically either focus on one-shot case, or separate detection from response decisions. Such practices lead to tractable analysis, but miss key inherent APTs persistence and risk heterogeneity. To this end, we propose a Lyapunov-based security-aware defense mechanism backed by threat intelligence, where robust defense strategy-making is based on acquired heterogeneity knowledge. By exploring temporal evolution of risk level, we introduce priority-aware virtual queues, which together with attack queues, enable security-aware response among hosts. Specifically, a long-term time average profit maximization problem is formulated. We first develop risk admission control policy to accommodate hosts' risk tolerance and response capacity. Under multiple attacker resources, defense control policy is implemented on two-stage decisions, involving proportional fair resource allocation and host-attack assignment. In particular, distributed auction-based assignment algorithm is designed to capture uncertainty in the number of resolved attacks, where high-risk host-attack pairs are prioritized over others. We theoretically prove our mechanism can guarantee bounded queue backlogs, profit optimality, no underflow condition, and robustness to detection errors. Simulations on real-world data set corroborate theoretical analysis and reveal the importance of security awareness.

The scarcity of efficient charging infrastructures has suppressed penetration rate of Electric Vehicles (EVs). This paper mainly focuses on the Fast Charging Station (FCS) placement problem, especially with the elastic demand. We first propose the distance preference and waiting time preference to capture the elastic EV charging demand. Moreover, a fixed-point equation is proposed to illustrate the relationship between serving demand rate and waiting time at station. We further formulate the problem of FCS placement with elastic demand as a bi-level optimization problem. In the upper level, we propose a heuristic algorithm to determine the optimal location of charging stations, with the goal of maximizing overall system profit. In the lower level, queueing theory is utilized to analyze the optimal capacity of each charging station. The sufficient condition for the existence of optimal station capacity is given. Simulation results demonstrate the effectiveness of our approach in improving system profit and reducing demand loss rate.

In this paper, we propose a novel game-based incentive mechanism for multi-resource sharing, where users are motivated to share their idle resources in view of conditional voluntary. Through social networking service platforms, such a crowdsourcing service fully explores the significant influence and computing potential of mobile social networks. Specifically, a combination of task allocation process, profit transfer process, and reputation updating process are involved in this sharing incentive mechanism, satisfying truthfulness, individual rationality, and robustness. To maintain the social fairness-efficiency tradeoff, we further develop a resource sharing algorithm on the basis of dominant resource fairness, revealing that the sacrifice of fairness properties is necessary for the improvement of efficiency. Real-world traces from Facebook are numerically studied, validating social fairness and efficiency of our social crowdsourcing mechanism.

To accommodate exponentially increasing traffic demands, operators are seeking to offload cellular traffic to small base stations (BSs) in heterogeneous cellular networks (HCNs), which is promising in alleviating traffic congestion. In HCNs, operators are eager to balance the traffic globally, where users may be pushed to less preferred small BSs, resulting in possible conflict with user local preference. Thus, it is a big challenge to achieve dynamic load balancing for operators and provide participation incentive for users simultaneously. Due to the dynamics of network state and user traffic demand, we are inspired to utilize Lyapunov optimization to develop a congestion-aware cellular offloading scheme. Specifically, an operator profit maximization problem involving network selection and rate control is formulated. To achieve long-term network stability, we propose a congestion-aware network selection algorithm, obtaining the BS alternative set that maintains traffic congestion constraint. By exploring the heterogeneity of user quality sensitivity, we devise the optimal quality-price contract, which maximizes operator profit. With effective pricing and resource allocation, users are motivated to make proper association strategy chosen from the BS alternative set. Simulation results demonstrate the effectiveness of our scheme in improving operator profit. User incentive and network stability are also validated.

Device-to-device (D2D) communication underlaying cellular networks is a promising technology for improving network resource utilization, and cooperative communication technology is usually used to mitigate the interference caused by D2D communication. Due to the additional signal processing cost introduced by cooperative communication, the cellular links who have the exclusive usage right of the network spectrum can charge the D2D links a fee for spectrum usage to enhance their profit. In this paper, we propose a contract-based cooperative spectrum sharing mechanism to exploit transmission opportunities for the D2D links and meanwhile achieve the maximum profit of the cellular links. We first design a cooperative relaying scheme that employs superposition coding at both the cellular transmitters and D2D transmitters. The cooperative relaying scheme can maximize the data rate of the D2D links without deteriorating the performance of the cellular links. Then, we employ a contract-theoretic framework to model the spectrum trading process based on the cooperative relaying scheme, and derive the optimal power-payment contracts for the cellular links under both the cases that the private information (i.e., channel quality) of the D2D links is continuous and discrete using tools from continuous-and discrete-time optimal control theories, respectively. Analytic and numerical results confirm the efficiency of the proposed spectrum sharing mechanism.

Delayed traffic offloading is a promising paradigm to alleviate the cellular network congestion caused by explosive traffic demands. As we all know, in mobile networks, the delay profile for traffic is remarkable due to users’ mobility. How to exploit user delay tolerance to improve the profit of operator as well as mobile users becomes a big challenge. In this paper, we model this delayed offloading process as a monopoly market based on contract theory, where operator acts as the monopolist setting up the optimal contract by statistical information on user satisfaction. We propose an incentive mechanism to motivate users to leverage their delay and price sensitivity in exchange for service cost. To capture the heterogeneity of user satisfaction, we classify users into different types. Each user chooses a proper quality–price contract item according to its type. More specifically, we investigate this delayed offloading scheme under strongly incomplete information scenario, where user type is private information. We derive an optimal contract, which maximizes operator’s profit for both the continuous-user-type model and the discrete-user-type model. Numerical results validate the effectiveness of our incentive mechanism for delayed traffic offloading in cellular networks.

Traffic offloading over Delay Tolerant Networks (DTNs) is a promising paradigm to alleviate the network congestion caused by explosive traffic demands. As we all know, in mobile networks, the delay profile for traffic is remarkable due to user's mobility. How to exploit delay tolerance to improve the profit of the operator as well as mobile users becomes a big challenge. In this paper, we investigate the problem of the interrelation of delay and user QoS. Inspired by contract theory, we model the delayed offloading process as a monopoly market where the operator makes pricing by considering statistical information about user satisfaction. In addition, we propose an incentive framework to motivate users to leverage their delay and price sensitivity in exchange for service cost. To capture the heterogeneity of user satisfaction, we classify users into different types. Each user chooses an appropriate quality-price contract item according to its type. Moreover, we derive an optimal contract which is feasible and maximizes the operator's profit as well. Numerical results validate the effectiveness of our incentive framework for traffic offloading over DTNs.