mlsanityData to 14 Oct 2026

Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks

arXiv 2108.08768 ↗First posted 16 Aug 2021cs.DC cs.LGcited by 7

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Abstract (arXiv)

Clustered Federated Multitask Learning (CFL) was introduced as an efficient scheme to obtain reliable specialized models when data is imbalanced and distributed in a non-i.i.d. (non-independent and identically distributed) fashion amongst clients. While a similarity measure metric, like the cosine similarity, can be used to endow groups of the client with a specialized model, this process can be arduous as the server should involve all clients in each of the federated learning rounds. Therefore, it is imperative that a subset of clients is selected periodically due to the limited bandwidth and latency constraints at the network edge. To this end, this paper proposes a new client selection algorithm that aims to accelerate the convergence rate for obtaining specialized machine learning models that achieve high test accuracies for all client groups. Specifically, we introduce a client selection approach that leverages the devices' heterogeneity to schedule the clients based on their round latency and exploits the bandwidth reuse for clients that consume more time to update the model. Then, the server performs model averaging and clusters the clients based on predefined thresholds. When a specific cluster reaches a stationary point, the proposed algorithm uses a greedy scheduling algorithm for that group by selecting the clients with less latency to update the model. Extensive experiments show that the proposed approach lowers the training time and accelerates the convergence rate by up to 50% while imbuing each client with a specialized model that is fit for its local data distribution.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 16 Aug 2021.

Builds on

16 of 22 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Learning with Non-IID Datacited by 1,108
  3. 2018LEAF: A Benchmark for Federated Settingscited by 792
  4. 2017Federated Multi-Task Learningcited by 672
  5. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  6. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  7. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  8. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  9. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  10. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  11. 2018Wireless Network Intelligence at the Edgecited by 92
  12. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
4 more
  1. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  2. 2020Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocationcited by 18
  3. 2021Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learningcited by 7
  4. 2021Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learningcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 2; 2025: 1; 2026: 1.

7 papers.

  1. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  2. 2025Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacycited by 4
  3. 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3
  4. 2021EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edgecited by 1
  5. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  6. 2023A Joint Gradient and Loss Based Clustered Federated Learning Designcited by 0
  7. 2026Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddingscited by 0