Stake-Driven Rewards and Log-Based Free Rider Detection in Federated Learning

Federated learning has become increasingly popular due to its ability to bring together multiple learners, enhance model generalizability, and promote knowledge exchange. Such systems inherently rely on the bedrock of security, trust, and fairness among training workers to ensure a conducive learning environment. However, this collaborative landscape has encoun-tered the challenge of free riders, individuals who join the systems to gain benefits without making any substantial contributions. This can negatively impact learning outcomes, fairness, sustain-ability, and trust in a collaborative system. In this paper, we first present a novel stake-based incentive mechanism aimed at promoting active participation among contributors, and con-currently maximizing the reward for clients with consideration of free rider presence in the system. Second, we propose an efficient method for identifying free riders in federated learning based on log analysis. Our method delegates the detection of free riders to training workers and the identification to the aggregator, rather than relying solely on the aggregator. We simulate potential deceptive strategies employed by free riders and assess the extent of our method's coverage across these scenarios. The experimental results conducted on different free rider ratios demonstrate the versatility and applicability of our approach in detecting these clients within the federated learning paradigm.