mlsanityData to 14 Oct 2026

Communication-Efficient Federated Learning with Adaptive Number of Participants

arXiv 2508.13803 ↗First posted 19 Aug 2025cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Rapid scaling of deep learning models has enabled performance gains across domains, yet it introduced several challenges. Federated Learning (FL) has emerged as a promising framework to address these concerns by enabling decentralized training. Nevertheless, communication efficiency remains a key bottleneck in FL, particularly under heterogeneous and dynamic client participation. Existing methods, such as FedAvg and FedProx, or other approaches, including client selection strategies, attempt to mitigate communication costs. However, the problem of choosing the number of clients in a training round remains extremely underexplored. We introduce Intelligent Selection of Participants (ISP), an adaptive mechanism that dynamically determines the optimal number of clients per round to enhance communication efficiency without compromising model accuracy. We validate the effectiveness of ISP across diverse setups, including vision transformers, real-world ECG classification, and training with gradient compression. Our results show consistent communication savings of up to 30% without losing the final quality. Applying ISP to different real-world ECG classification setups highlighted the selection of the number of clients as a separate task of federated learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 19 Aug 2025.

Builds on

22 of 34 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  7. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  8. 2017Federated Multi-Task Learningcited by 672
  9. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  10. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  11. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
  12. 2019A Survey on Distributed Machine Learningcited by 188
10 more
  1. 2019Automatic diagnosis of the 12-lead ECG using a deep neural networkcited by 164
  2. 2020Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XLcited by 158
  3. 2017Revisiting Distributed Synchronous SGDcited by 142
  4. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  5. 2019Active Federated Learningcited by 63
  6. 2021Rethinking gradient sparsification as total error minimizationcited by 41
  7. 2022FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformerscited by 33
  8. 2022Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reductioncited by 32
  9. 2022Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraintscited by 30
  10. 2021Dynamic Attention-based Communication-Efficient Federated Learningcited by 9

Built on by

Not cited yet.