Incentive-Aware Autonomous Client Participation in Federated Learning

Abstract

Federated learning (FL) emerges as a promising paradigm to enable a federation of clients to train a machine learning model in a privacy-preserving manner. Most existing works assumed that the central parameter server (PS) determines the participation of clients implying that clients cannot make autonomous participation decisions. The above assumption is unrealistic because the participation in FL training may incur various cost and clients also have strong desire to be rewarded for participation. To address this problem, we design a novel autonomous client participation scheme to incentivize clients. Specifically, the PS provides a certain reward shared among participating clients for each training round. Clients decide whether to participate each FL training round or not based on their own utilities (i.e., reward minus cost). The process can be modeled as a minority game (MG) with incomplete information and clients end up in the minority side win after each training round because the reward of each participating client may not cover its cost if too many clients participate and vice verse. The challenge of autonomous participation schemes lies in lowering the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">volatility</i> of participating clients in each round due to the lack of coordination among clients. Through solid analysis, we prove that: 1) The volatility of participating clients in each round is very high under the standard MG scheme. 2) The volatility of participating clients can be reduced significantly under the stochastic MG scheme. 3) A coalition based MG is proposed, which can further reduce the volatility in each round. By conducting extensive experiments in real settings, we demonstrate that the stochastic MG-based scheme outperforms other state-of-the-art algorithms in terms of utility and volatility, and the coalition MG-based client participation scheme can further boost the utility by 39%-48% and reduce the volatility by 51%–100%. Moreover, our algorithms can achieve almost the same model accuracy as that obtained by centralized client participation algorithms.