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.

With the global proliferation of electric vehicles, battery state monitoring has emerged as a foundation for ensuring operational safety and reliability. However, in real-world applications, battery systems operate within complex dynamic spatiotemporal contexts, characterized by non-stationary aging drift in the temporal dimension and cross-scale heterogeneity in the spatial dimension. While existing reviews extensively cover specific parameter estimation, most studies rely on static assumptions or isolated technical paths, failing to capture the deep spatiotemporal coupling mechanisms or meet the privacy and bandwidth constraints of distributed vehicular environments. To bridge this gap, this article provides a comprehensive review of battery monitoring algorithms, organized by temporal, spatial, and spatiotemporal dimensions. Furthermore, to address the limitations of centralized paradigms in managing heterogeneous data, we analyze a decoupling-interaction framework enabled by federated learning. By shifting the monitoring paradigm toward cloud-edge collaborative intelligence, this framework facilitates localized spatiotemporal decoupling for heterogeneous data adaptation and cloud-based interaction for global evolutionary pattern mining. Finally, we offer insights into the open challenges of practical deployment, and further discuss the transition of monitoring algorithms toward semantic reasoning via foundation models, physics-constrained communication, and generative continual learning. This provides a systematic research perspective and future direction for battery state monitoring within dynamic spatiotemporal contexts.

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%.

In this paper, we propose a novel physical-layer key generation (PKG) scheme for orthogonal frequency division multiplexing-orbital angular momentum (OFDM-OAM) systems to significantly enhance the confidentiality capacity (CC). In the proposed scheme, we first establish the OAM channel model under uniform circular array (UCA) misalignment in the line-of-sight (LoS) channel. Facing the risk of information leakage during key negotiation, we couple key generation with the OFDM communication process. Then, we analyze the CC of the OFDM-OAM system and derive its closed-form expression. Simulation results illustrate that the proposed OFDM-OAM PKG scheme has achieved high CC compared with existing works. In addition, as the offset angle of the eavesdropper’s UCA increases, the CC increases, and the bit error rate (BER) of the eavesdropper tends to be 0.5.

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.