# SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead

arXiv: https://arxiv.org/abs/1910.01355 · HTML: https://mlsanity.com/paper/1910.01355/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 30 of 82)

- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [FederatedScope: A Flexible Federated Learning Platform for Heterogeneity](https://mlsanity.com/paper/2204.05011/) (2022)
- [Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2404.06448/) (2024)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training](https://mlsanity.com/paper/2304.06947/) (2023)
- [Resource-Efficient Federated Learning](https://mlsanity.com/paper/2111.01108/) (2021)
- [Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things](https://mlsanity.com/paper/2103.02762/) (2021)
- [Context-Aware Online Client Selection for Hierarchical Federated Learning](https://mlsanity.com/paper/2112.00925/) (2021)
- [CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2112.08909/) (2021)
- [AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation](https://mlsanity.com/paper/2205.13797/) (2022)
- [IronForge: An Open, Secure, Fair, Decentralized Federated Learning](https://mlsanity.com/paper/2301.04006/) (2023)
- [AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices](https://mlsanity.com/paper/2312.10935/) (2023)
- [FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler](https://mlsanity.com/paper/2309.14675/) (2023)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity](https://mlsanity.com/paper/2112.10313/) (2021)
- [Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout](https://mlsanity.com/paper/2210.16105/) (2022)
- [Artificial Intelligence of Things: A Survey](https://mlsanity.com/paper/2410.19998/) (2024)
- [Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks](https://mlsanity.com/paper/2301.06646/) (2023)
- [CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework](https://mlsanity.com/paper/2104.08184/) (2021)
- [Secure and Efficient Federated Learning Through Layering and Sharding Blockchain](https://mlsanity.com/paper/2104.13130/) (2021)
- [GitFL: Adaptive Asynchronous Federated Learning using Version Control](https://mlsanity.com/paper/2211.12049/) (2022)
- [Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations](https://mlsanity.com/paper/2202.13606/) (2022)
- [Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data](https://mlsanity.com/paper/2203.01214/) (2022)
- [Communication-Efficient Federated Learning With Data and Client Heterogeneity](https://mlsanity.com/paper/2206.10032/) (2022)
- [Efficient Asynchronous Federated Learning with Sparsification and Quantization](https://mlsanity.com/paper/2312.15186/) (2023)
- [Byzantine-Resilient Federated Learning at Edge](https://mlsanity.com/paper/2303.10434/) (2023)
- [Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning](https://mlsanity.com/paper/2311.13163/) (2023)
- [HADFL: Heterogeneity-aware Decentralized Federated Learning Framework](https://mlsanity.com/paper/2111.08274/) (2021)
- [Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks](https://mlsanity.com/paper/2209.13115/) (2022)
- [FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting](https://mlsanity.com/paper/2406.02877/) (2024)

## 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.