A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning

Tongyang Xu, Yuan Liu, Zhaotai Ma, Yiqiang Huang and Peng Liu ()
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Tongyang Xu: College of Computer and Control Engineering, Northeast Forestry University, Hexing Road 26, Harbin 150040, China
Yuan Liu: College of Computer and Control Engineering, Northeast Forestry University, Hexing Road 26, Harbin 150040, China
Zhaotai Ma: College of Computer and Control Engineering, Northeast Forestry University, Hexing Road 26, Harbin 150040, China
Yiqiang Huang: College of Computer and Control Engineering, Northeast Forestry University, Hexing Road 26, Harbin 150040, China
Peng Liu: College of Computer and Control Engineering, Northeast Forestry University, Hexing Road 26, Harbin 150040, China

Future Internet, 2023, vol. 15, issue 6, 1-19

Abstract: As a new distributed machine learning (ML) approach, federated learning (FL) shows great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogeneity in terms of data distribution and hardware configurations make it hard to select participants from the thousands of nodes. In this paper, we propose a multi-objective node selection approach to improve time-to-accuracy performance while resisting malicious nodes. We firstly design a deep reinforcement learning-assisted FL framework. Then, the problem of multi-objective node selection under this framework is formulated as a Markov decision process (MDP), which aims to reduce the training time and improve model accuracy simultaneously. Finally, a Deep Q-Network (DQN)-based algorithm is proposed to efficiently solve the optimal set of participants for each iteration. Simulation results show that the proposed method not only significantly improves the accuracy and training speed of FL, but also has stronger robustness to resist malicious nodes.

Keywords: federated learning; node selection; deep reinforcement learning; multi-objective; model performance (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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