2021/07/23 by Chung-Hsuan Hu, Zheng Chen, Hu, Chung-Hsuan +3
Computer Science · #Age of Information Optimization #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Machine Learning (cs.LG) #Parallel #Privacy-Preserving Technologies in Data #Signal Processing (eess.SP) #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2107.11415
openalex publication_date 2021/07/23 · openalex created_date 2021/08/02 · openalex updated_date 2026/07/28
Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework that combines on-device local training with server-based model synchronization to train a centralized ML model over distributed nodes. In this paper, we propose an asynchronous FL framework with periodic aggregation to eliminate the straggler issue in FL systems. For the proposed model, we investigate several device scheduling and update aggregation policies and compare their performances when the devices have heterogeneous computation capabilities and training data distributions. From the simulation results, we conclude that the scheduling and aggregation design for asynchronous FL can be rather different from the synchronous case. For example, a norm-based significance-aware scheduling policy might not be efficient in an asynchronous FL setting, and an appropriate "age-aware" weighting design for the model aggregation can greatly improve the learning performance of such systems.