FairReward: Towards Fair Reward Distribution using Equity Theory in Blockchain-based Federated Learning

Ensuring fairness in incentive mechanisms for federated learning (FL) is essential to attracting high-quality clients and building a sustainable FL ecosystem. Most existing fairness-aware incentive mechanisms distribute rewards to FL clients by quantifying their contributions to the performance of the global model. Essentially, these mechanisms pursue contribution fairness, namely a constant contribution-reward ratio across FL clients, with an implicit assumption that clients would be satisfied with the contribution fairness. However, research in social psychology has confirmed that this assumption may not hold in many real-world scenarios. According to equity theory proposed by Adams, an individual's assessment and perception of receiving fair treatment significantly depend on the input-outcome ratio, where outcome simply refers to the rewards, while input is far more complex because it involves a bunch of subtle factors such as enthusiasm, experience and tolerance as well as the estimated contributions. Inspired by Adams' equity theory, in this work, we expand the notion of contribution fairness to input fairness and propose a new fairness-aware incentive mechanism named FairReward that distributes rewards under the joint consideration of self-reported inputs and computed contributions. FairReward employs a reputation mechanism to enhance the credibility of self-reported inputs and leverages blockchains to eliminate the need of a trusted FL server and monetarily incentivize/penalize clients. In addition, FairReward adopts techniques including distributed differential privacy and locality-sensitive hashing to address privacy and non-IID issues in FL. Moreover, we conduct a comprehensive security and privacy analysis. Finally, we evaluate FairReward through extensive experiments. The comprehensive experimental results demonstrate that FairReward is effective, scalable and attack-resistant, and provides the input fairness required.