SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
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Abstract (arXiv)
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost.
Key facts. Cited by 82 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2019.
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Citing papers per year: 2021: 19; 2022: 15; 2023: 19; 2024: 14; 2025: 12; 2026: 3.
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- 2025Accelerating MoE Model Inference with Expert Shardingcited by 0
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- 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
- 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
- 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
- 2022A Survey on Heterogeneous Federated Learningcited by 42
- 2023TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Trainingcited by 27
- 2021Resource-Efficient Federated Learningcited by 26
- 2021Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Thingscited by 20
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- 2021CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learningcited by 20
- 2022AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregationcited by 19
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- 2023AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devicescited by 15
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- 2026Efficiently Distributed Federated Learning17 Sep 2026
- 2026FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning17 Feb 2026
- 2025Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things28 Nov 2025
- 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice14 Oct 2025
- 2025Private Federated Multiclass Post-hoc Calibration2 Oct 2025
- 2025Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset3 Aug 2025
- 2025Efficient Federated Learning with Timely Update Dissemination8 Jul 2025
- 2025RIFLES: Resource-effIcient Federated LEarning via Scheduling19 May 2025
- 2025FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing7 Apr 2025
- 2025Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible5 Apr 2025
- 2025MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments3 Mar 2025
- 2025SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training22 Feb 2025
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