FedSC: Game-Theoretic Design of Sustainable Contracts for Unreliable Federated Edge Learning

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.