Client Selection in Federated Learning: A Dynamic Matching-Based Incentive Mechanism

Federated learning (FL) has rapidly evolved as a distributed learning paradigm, enabling clients to collaboratively train models while retaining data privacy on their devices, which can guarantee the privacy of the training data. However, it faces distinct challenges on both server and client fronts. On the server side, there is a lack of efficient strategies for selecting high-performing clients, leading to potential degradation in training accuracy due to subpar model updates. On the client's side, they are often deterred from participation due to significant energy consumption during both computation and data transmission processes. Existing incentive mechanisms in FL seldom consider both the energy consumption of the clients and the learning quality of the server. To bridge this gap, this paper introduces an adaptive incentive mechanism, which considers both the anticipated learning quality of clients and the associated energy costs during training. We propose a novel distributed Matching-based Incentive Mechanism (MAAIM) for client selection in FL. Leveraging a deferred acceptance algorithm, MAAIM facilitates stable client-server pairings, ensuring that both parties' primary concerns are addressed. Experimental results demonstrate the effectiveness of the proposed MAAIM.