mlsanityData to 14 Oct 2026

Fast Heterogeneous Federated Learning with Hybrid Client Selection

arXiv 2208.05135 ↗First posted 10 Aug 2022cs.LG cs.AIcited by 8

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Abstract (arXiv)

Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL). However, the large variance of the model updates aggregated from the randomly-selected unrepresentative subsets directly slows the FL convergence. We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction. Simple yet effective schemes are designed to improve the clustering effect and control the effect fluctuation, therefore, generating the client subset with certain representativeness of sampling. Theoretically, we demonstrate the improvement of the proposed scheme in variance reduction. We also present the tighter convergence guarantee of the proposed method thanks to the variance reduction. Experimental results confirm the exceed efficiency of our scheme compared to alternatives.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 27 of its 34 references that are in the map. First posted on arXiv 10 Aug 2022.

Builds on

27 of 34 references.

References · 27

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  7. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  8. 2018Local SGD Converges Fast and Communicates Littlecited by 605
  9. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  10. 2018Sparsified SGD with Memorycited by 376
  11. 2018Not All Samples Are Created Equal: Deep Learning with Importance Samplingcited by 328
  12. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
15 more
  1. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
  2. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 195
  3. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 154
  4. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 153
  5. 2020Optimal Client Sampling for Federated Learningcited by 124
  6. 2019Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronizationcited by 107
  7. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 96
  8. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  9. 2017On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimizationcited by 92
  10. 2014Stochastic Optimization with Importance Samplingcited by 91
  11. 2021MARINA: Faster Non-Convex Distributed Learning with Compressioncited by 78
  12. 2020Optimal Importance Sampling for Federated Learningcited by 20
  13. 2020Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learningcited by 14
  14. 2021Federated Expectation Maximization with heterogeneity mitigation and variance reductioncited by 5
  15. 2019SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimizationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 2; 2026: 1.

8 papers.

  1. 2023V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selectioncited by 5
  2. 2023Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variancecited by 2
  3. 2024Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learningcited by 2
  4. 2025FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selectioncited by 1
  5. 2025Energy-Efficient Federated Learning for AIoT using Clustering Methodscited by 1
  6. 2023Intelligent Client Selection for Federated Learning using Cellular Automatacited by 0
  7. 2024DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learningcited by 0
  8. 2026Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learningcited by 0