DRL-Based Adaptive Time Threshold Client Selection FL
Sreyleak Sam, Taikuong Iv, Rothny Mom, Seungwoo Kang, Inseok Song, Seyha Ros, Sovanndoeur Riel, Seokhoon Kim · Symmetry · 2025
Federated Learning (FL) has been proposed as a new machine learning paradigm to ensure data privacy by training the model in a decentralized manner. However, FL is challenged by device heterogeneity, asymmetric data contribution, and imbalanced datasets, which complicate system control and hinder performance due to long waiting times for aggregation. To tackle the FL challenges, we propose Adaptive Time Threshold Client Selection using DRL (ATCS-FL) to adjust the time threshold (α) in each communication round based on computing and resource capacity of each device and the volume of data updates. The Double Deep Q-Network (DDQN) model determines the appropriate α, according to the variations in local training time that achieves performance improvement alongside latency reduction. Based on the α, the server selects a subset of clients with adequate resources that can finish training within the α for participating in the training process. Our approach dynamically adjusts the α and adaptively selects the number of clients, effectively mitigates the impact of heterogeneous training speeds and significantly enhances communication efficiency. Our experiment utilizes CIFAR-10 and MNIST benchmarked datasets for image classification training with convolutional neural networks across non-IID distributed levels in FL. Specifically, ATCS-FL demonstrates performance improvement and latency reduction of 77% and 75%, respectively, compared to FedProx and FLASH-RL.