mlsanityData to 7 Oct 2026

Fast-Convergent Federated Learning via Cyclic Aggregation

arXiv 2210.16520 ↗First posted 29 Oct 2022cs.LG eess.SPcited by 6

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Abstract (arXiv)

Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server. While it is theoretically well-known that FL yields an optimal model -- centrally trained model assuming availability of all the edge device data at the central server -- under mild condition, in practice, it often requires massive amount of iterations until convergence, especially under presence of statistical/computational heterogeneity. This paper utilizes cyclic learning rate at the server side to reduce the number of training iterations with increased performance without any additional computational costs for both the server and the edge devices. Numerical results validate that, simply plugging-in the proposed cyclic aggregation to the existing FL algorithms effectively reduces the number of training iterations with improved performance.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 29 Oct 2022.

Builds on

17 of 25 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2018Federated Learning with Non-IID Datacited by 1,099
  9. 2021Model-Contrastive Federated Learningcited by 762
  10. 2015Cyclical Learning Rates for Training Neural Networkscited by 627
  11. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  12. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
5 more
  1. 2017On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimizationcited by 91
  2. 2021Super-Acceleration with Cyclical Step-sizescited by 14
  3. 2017Exploring loss function topology with cyclical learning ratescited by 11
  4. 2022General Cyclical Training of Neural Networkscited by 5
  5. 2022Compressed Particle-Based Federated Bayesian Learning and Unlearningcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2025A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical Imagingcited by 3
  2. 2025Debunking Optimization Myths in Federated Learning for Medical Image Classificationcited by 3
  3. 2025Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learningcited by 2
  4. 2024Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networkscited by 0
  5. 2024Embedding Byzantine Fault Tolerance into Federated Learning via Consistency Scoringcited by 0
  6. 2026Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learningcited by 0