FedAB: Truthful Federated Learning With Auction-Based Combinatorial Multi-Armed Bandit

Federated learning (FL) emerges as a new distributed machine learning (ML) paradigm that enables thousands of mobile devices to collaboratively train ML models using local data without compromising user privacy. However, the FL learning quality highly relies on the data contribution from the distributed mobile devices. Therefore, a well-designed incentive mechanism with effectiveness, fairness, and reciprocity is in urgent need to guarantee the stable participation of users. In this article, we propose federated auction bandit (FedAB), an incentive and client selection strategy based on a novel multiattribute reverse auction mechanism and a combinatorial multi-armed bandit (CMAB) algorithm. First, we develop a local contribution evaluation method based on importance sampling in the FL context. We then design a novel payment mechanism that is able to preserve individual rationality and incentive compatibility (truthfulness). At last, we design a UCB-based winner selection algorithm that is proven to achieve the server's utility maximization with fairness and reciprocity. We have conducted extensive experiments on real data sets. The results demonstrate the superiority of FedAB, with a 10%-50% improvement in total reward, final accuracy, and convergence speed compared to state-of-the-art solutions.

Automated summary

In this article, we propose FedAB, an incentive and client selection strategy for federated learning, which ensures the effectiveness, fairness, and reciprocity of data contribution while preserving user privacy. Extensive experiments on real datasets demonstrate the superiority of FedAB in terms of total reward, final accuracy, and convergence speed compared to state-of-the-art solutions.