A Fairness-aware Incentive Framework for Heterogeneous Federated Learning with Bifurcated Reverse Auction Design

Federated Learning (FL) is an emerging distributed learning framework designed to address isolated data island and protect privacy. Besides, Clustered Federated Learning (CFL) is introduced as an efficient multitask scheme to solve heterogeneous problems in FL where clients' data is distributed in non-i.i.d. (non-independent and identically distributed) scenarios. However, due to bandwidth limitation and latency tolerance, the server can only select a subset of clients to participate. Average selection and only selecting low heterogeneous client groups lead to severe results. How to fairly select clients and improve efficient model performance in heterogeneous scenarios with limited communication has become a key issue. We propose a fairness-aware clustered federated learning (FACFL) incentive framework which balances collective and individual fairness. Specifically, our framework models CFL as a bifurcated reverse auction that consists of a first-layer cluster auction and a second-layer client auction. Our framework can dynamically adjust the par-ticipation of clusters and clients according to the communication capabilities. The experimental results on the CIFAR-10 dataset demonstrate that FACFL improves the model performance in severely heterogeneous and communication limited scenarios. Additionally, FACFL can maintain a high level of the training fairness with different numbers of clients.