Xu, T.; Liu, Y.; Ma, Z.; Huang, Y.; Liu, P. A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning. Future Internet2023, 15, 209.
Xu, T.; Liu, Y.; Ma, Z.; Huang, Y.; Liu, P. A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning. Future Internet 2023, 15, 209.
Xu, T.; Liu, Y.; Ma, Z.; Huang, Y.; Liu, P. A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning. Future Internet2023, 15, 209.
Xu, T.; Liu, Y.; Ma, Z.; Huang, Y.; Liu, P. A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning. Future Internet 2023, 15, 209.
Abstract
As a new distributed machine learning (ML) approach, federated learning (FL) shows the 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-netwok (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 has stronger robustness to resist malicious nodes.
Keywords
Federated Learning; Node Selection; Deep Reinforcement Learning; Multi-Objective; Model Performance
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
