Cost-Aware Federated Learning in Mobile Edge Networks

Federated Learning (FL) allows multiple heterogeneous clients to cooperatively train models without disclosing private data. However, selfish clients may be unwilling to participate in FL training without any compensation. In addition, the characteristics of mobile edge networks may also reduce the efficiency of FL training and increase training cost. To solve these challenges, this paper proposes a Cost-Aware FL framework with client incentive and model compression (CAFL), aiming to incentivize clients to participate in FL training and reduce training cost. We employ the reverse auction for incentive design, and model the processes of client selection, local training, and model compression as Mixed-Integer Non-Linear Programming problem. Accordingly, we propose an improved Soft Actor-Critic-based client selection and model compression algorithm to solve the optimization problem, and design a Vickrey-Clarke-Groves-based payment rule to compensate for clients' cost. Finally, the simulation experiment results show that the proposed method outperforms other benchmarks in terms of BS's cost under various scenarios.