SACW: Semi-Asynchronous Federated Learning with Client Selection and Adaptive Weighting

Federated learning (FL), as a privacy-preserving distributed machine learning paradigm, demonstrates unique advantages in addressing data silo problems. However, the prevalent statistical heterogeneity (data distribution disparities) and system heterogeneity (device capability variations) in practic...

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Published in:Computers (Basel) Vol. 14; no. 11; p. 464
Main Authors: Li, Shuaifeng, Shan, Fangfang, Mao, Shiqi, Lu, Yanlong, Miao, Fengjun, Chen, Zhuo
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2025
Subjects:
Adaptive algorithms
adaptive weighting
Algorithms
Artificial intelligence
client clustering
client selection
Clustering
Communication
Distributed processing (Computers)
Efficiency
Electronic data processing
Federated learning
File servers
Heterogeneity
Homogeneity
Information management
Internet of Things
Machine learning
Methods
Resource utilization
semi-asynchronous federated learning
Servers
statistical heterogeneity
Synchronism
system heterogeneity
Weighting
Florida
New York
China
ISSN:2073-431X, 2073-431X
Online Access:Get full text
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Summary:Federated learning (FL), as a privacy-preserving distributed machine learning paradigm, demonstrates unique advantages in addressing data silo problems. However, the prevalent statistical heterogeneity (data distribution disparities) and system heterogeneity (device capability variations) in practical applications significantly hinder FL performance. Traditional synchronous FL suffers from severe waiting delays due to its mandatory synchronization mechanism, while asynchronous approaches incur model bias issues caused by training pace discrepancies. To tackle these challenges, this paper proposes the SACW framework, which effectively balances training efficiency and model quality through a semi-asynchronous training mechanism. The framework adopts a hybrid strategy of “asynchronous client training–synchronous server aggregation,” combined with an adaptive weighting algorithm based on model staleness and data volume. This approach significantly improves system resource utilization and mitigates system heterogeneity. Simultaneously, the server employs data distribution-aware client clustering and hierarchical selection strategies to construct a training environment characterized by “inter-cluster heterogeneity and intra-cluster homogeneity.” Representative clients from each cluster are selected to participate in model aggregation, thereby addressing data heterogeneity. We conduct comprehensive comparisons with mainstream synchronous and asynchronous FL methods and perform extensive experiments across various model architectures and datasets. The results demonstrate that SACW achieves better performance in both training efficiency and model accuracy under scenarios with system and data heterogeneity.
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ISSN:2073-431X
2073-431X
DOI:10.3390/computers14110464