mlsanityData to 7 Oct 2026

CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework

arXiv 2104.08184 ↗First posted 16 Apr 2021cs.LG cs.DCcited by 10

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Abstract (arXiv)

Federated learning (FL) is an emerging distributed machine learning paradigm that protects privacy and tackles the problem of isolated data islands. At present, there are two main communication strategies of FL: synchronous FL and asynchronous FL. The advantages of synchronous FL are that the model has high precision and fast convergence speed. However, this synchronous communication strategy has the risk that the central server waits too long for the devices, namely, the straggler effect which has a negative impact on some time-critical applications. Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash. Therefore, we combine the advantages of these two strategies to propose a clustered semi-asynchronous federated learning (CSAFL) framework. We evaluate CSAFL based on four imbalanced federated datasets in a non-IID setting and compare CSAFL to the baseline methods. The experimental results show that CSAFL significantly improves test accuracy by more than +5% on the four datasets compared to TA-FedAvg. In particular, CSAFL improves absolute test accuracy by +34.4% on non-IID FEMNIST compared to TA-FedAvg.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 16 Apr 2021.

Builds on

19 of 35 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2018LEAF: A Benchmark for Federated Settingscited by 780
  7. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  8. 2019Asynchronous Federated Optimizationcited by 264
  9. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  10. 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 131
  11. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  12. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
7 more
  1. 2015Speeding Up Distributed Machine Learning Using Codescited by 76
  2. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  3. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  4. 2019Accelerating DNN Training in Wireless Federated Edge Learning Systemscited by 34
  5. 2020Coded Federated Learningcited by 14
  6. 2017Efficient Twitter Sentiment Classification using Subjective Distant Supervisioncited by 9
  7. 2019Asynchronous Online Federated Learning for Edge Devices with Non-IID Datacited by 9

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Cited by year

Citing papers per year: 2022: 3; 2023: 4; 2024: 2; 2025: 1.

10 papers.

  1. 2022FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneitycited by 20
  2. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  3. 2023FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Schedulercited by 13
  4. 2023Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networkscited by 11
  5. 2022Semi-Synchronous Personalized Federated Learning over Mobile Edge Networkscited by 5
  6. 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updatescited by 4
  7. 2022FedLesScan: Mitigating Stragglers in Serverless Federated Learningcited by 2
  8. 2023Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networkscited by 0
  9. 2024Towards Client Driven Federated Learningcited by 0
  10. 2025DAG-AFL:Directed Acyclic Graph-based Asynchronous Federated Learningcited by 0