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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contribution fairness</i>, namely a constant contribution-reward ratio across FL clients, with an implicit assumption that clients would be satisfied with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contribution fairness</i>. 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contribution fairness</i> to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">input fairness</i> and propose a new fairness-aware incentive mechanism named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FairReward</i> that distributes rewards under the joint consideration of self-reported inputs and computed contributions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FairReward</i> 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, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FairReward</i> 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FairReward</i> through extensive experiments. The comprehensive experimental results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FairReward</i> is effective, scalable and attack-resistant, and provides the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">input fairness</i> required.