mlsanityData to 7 Oct 2026

FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling

arXiv 2009.10748 ↗First posted 22 Sep 2020cs.LG math.OC stat.MLcited by 26

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Abstract (arXiv)

We develop FedCluster--a novel federated learning framework with improved optimization efficiency, and investigate its theoretical convergence properties. The FedCluster groups the devices into multiple clusters that perform federated learning cyclically in each learning round. Therefore, each learning round of FedCluster consists of multiple cycles of meta-update that boost the overall convergence. In nonconvex optimization, we show that FedCluster with the devices implementing the local {stochastic gradient descent (SGD)} algorithm achieves a faster convergence rate than the conventional {federated averaging (FedAvg)} algorithm in the presence of device-level data heterogeneity. We conduct experiments on deep learning applications and demonstrate that FedCluster converges significantly faster than the conventional federated learning under diverse levels of device-level data heterogeneity for a variety of local optimizers.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2020.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 5; 2024: 5; 2025: 2; 2026: 2.

26 papers.

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  1. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  2. 2022FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregationcited by 21
  3. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  4. 2023Exploiting Label Skews in Federated Learning with Model Concatenationcited by 14
  5. 2023Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombinationcited by 13
  6. 2021Compositional federated learning: Applications in distributionally robust averaging and meta learningcited by 12
  7. 2021Certifiably-Robust Federated Adversarial Learning via Randomized Smoothingcited by 9
  8. 2022Fast Adaptive Federated Bilevel Optimizationcited by 6
  9. 2022Multi-Model Federated Learning with Provable Guaranteescited by 5
  10. 2023CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularizationcited by 5
  11. 2021Federated Learning: Issues in Medical Applicationcited by 4
  12. 2022FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgmentcited by 3
14 more
  1. 2024Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approachcited by 3
  2. 2021Robust Convergence in Federated Learning through Label-wise Clusteringcited by 2
  3. 2022Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimizationcited by 2
  4. 2024CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balancecited by 2
  5. 2024Dataset Distillation-based Hybrid Federated Learning on Non-IID Datacited by 2
  6. 2022FedCAT: Towards Accurate Federated Learning via Device Concatenationcited by 1
  7. 2025Federated Learning for Commercial Image Sourcescited by 1
  8. 2022FedMR: Fedreated Learning via Model Recombinationcited by 0
  9. 2023RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligencecited by 0
  10. 2024KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgettingcited by 0
  11. 2024CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clusteringcited by 0
  12. 2025Secure Generalization through Stochastic Bidirectional Parameter Updates Using Dual-Gradient Mechanismcited by 0
  13. 2026When To Adapt? Adapting the Model or Data in Federated Medical Imagingcited by 0
  14. 2026Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaborationcited by 0