Bidirectional Selection for Federated Learning Incorporating Client Autonomy: An Accuracy-Aware Incentive Approach
Huaguang Shi, Yuxiang Tian, Hengji Li, Lei Shi, Yi Fan Zhou · IEEE Internet of Things Journal · 2024
Federated learning (FL) is a distributed learning framework that allows clients to build models without disclosing local data. However, in resource-constrained scenarios, it is costly to participate in FL for all clients. Hence, selection strategy should be designed to select the most appropriate client groups. Current selection strategies are mainly cost and accuracy oriented, ignoring the autonomy of clients, which leads to the inability of clients to make autonomous decisions when participating in model training and updating. To realize autonomous selection of clients, we design a novel model accuracy-aware bidirectional client selection (MABCS) algorithm. The MABCS algorithm implements selection from both server and client dimensions. Specifically, the server evaluates the contributions of clients and design an accuracy-aware dynamic incentive mechanism. The client measures participation autonomy based on the reward and cost to decide whether or not to participate in FL. Thus, the client selection problem is modeled as a joint nonconvex optimization problem that maximizes the system revenue by optimizing the selection strategy and resource allocation strategy. The block coordinate descent algorithm is utilized to decouple the selection strategy and resource allocation strategy, and a linear approximation is employed to transform the selection strategy problem into a convex problem. An alternating optimization algorithm is used for the subproblems after the decomposition to obtain a near-optimal solution. Simulation results indicate that the MABCS algorithm exhibits superior convergence performance compared with other benchmark schemes.