Communication-Efficient Federated Learning with Adaptive Number of Participants

• URL: http://arxiv.org/abs/2508.13803v2
• Date: Tue, 23 Sep 2025 17:13:57 GMT
• Title: Communication-Efficient Federated Learning with Adaptive Number of Participants
• Authors: Sergey Skorik, Vladislav Dorofeev, Gleb Molodtsov, Aram Avetisyan, Dmitry Bylinkin, Daniil Medyakov, Aleksandr Beznosikov,
• Abstract summary: Intelligent Selection of Participants (ISP) is an adaptive mechanism that dynamically determines the optimal number of clients per round.<n>We show consistent communication savings of up to 30% without losing the final quality.<n>Applying ISP to different real-world ECG classification setups highlighted the selection of the number of clients as a separate task of Federated Learning.
• Score: 40.546240088040214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices [16.902104043318975]
  Client-Centric Adaptation federated learning (FedCCA) is an algorithm that optimally utilizes client-specific knowledge to learn a unique model for each client.<n>We conduct extensive experiments on diverse datasets to assess the efficacy of FedCCA.
  arXiv  Detail & Related papers  (2026-01-25T06:01:19Z)
• An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning [3.683202928838613]
  Federated learning is a novel decentralized learning architecture.<n>We propose to dynamically adjust the number of clusters to find the most ideal grouping results.<n>It may reduce the number of users participating in the training to achieve the effect of reducing communication costs without affecting the model performance.
  arXiv  Detail & Related papers  (2025-04-11T08:43:12Z)
• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private.<n>We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• Adaptive Client Selection with Personalization for Communication Efficient Federated Learning [2.8484833657472644]
  Federated Learning (FL) is a distributed approach to collaboratively training machine learning models.<n>This article introduces ACSP-FL, a solution to reduce the overall communication and computation costs for training a model in FL environments.
  arXiv  Detail & Related papers  (2024-11-26T19:20:59Z)
• FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data [54.81695390763957]
  Federated learning is an emerging distributed machine learning method. We propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate. We show that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients.
  arXiv  Detail & Related papers  (2023-09-18T12:35:05Z)
• Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach [16.093068118849246]
  This paper presents a novel split federated learning (SFL) framework that pairs clients with different computational resources. A greedy algorithm is proposed by reconstructing the optimization of training latency as a graph edge selection problem. Simulation results show the proposed method can significantly improve the FL training speed and achieve high performance.
  arXiv  Detail & Related papers  (2023-08-26T11:10:54Z)
• DPP-based Client Selection for Federated Learning with Non-IID Data [97.1195165400568]
  This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) We first analyze the effect of CS in FL and show that FL training can be accelerated by adequately choosing participants to diversify the training dataset in each round of training. We leverage data profiling and determinantal point process (DPP) sampling techniques to develop an algorithm termed Federated Learning with DPP-based Participant Selection (FL-DP$3$S)
  arXiv  Detail & Related papers  (2023-03-30T13:14:54Z)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)
• Addressing Client Drift in Federated Continual Learning with Adaptive Optimization [10.303676184878896]
  We outline a framework for performing Federated Continual Learning (FCL) by using NetTailor as a candidate continual learning approach. We show that adaptive federated optimization can reduce the adverse impact of client drift and showcase its effectiveness on CIFAR100, MiniImagenet, and Decathlon benchmarks.
  arXiv  Detail & Related papers  (2022-03-24T20:00:03Z)
• Dynamic Attention-based Communication-Efficient Federated Learning [85.18941440826309]
  Federated learning (FL) offers a solution to train a global machine learning model. FL suffers performance degradation when client data distribution is non-IID. We propose a new adaptive training algorithm $textttAdaFL$ to combat this degradation.
  arXiv  Detail & Related papers  (2021-08-12T14:18:05Z)
• Stochastic Client Selection for Federated Learning with Volatile Clients [41.591655430723186]
  Federated Learning (FL) is a privacy-preserving machine learning paradigm. In each round of synchronous FL training, only a fraction of available clients are chosen to participate. We propose E3CS, a client selection scheme to solve the problem.
  arXiv  Detail & Related papers  (2020-11-17T16:35:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.