mlsanityData to 14 Oct 2026

Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach

arXiv 2310.19069 ↗First posted 29 Oct 2023cs.LG cs.DCcited by 1

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Abstract (arXiv)

Federated learning (FL) offers a decentralized training approach for machine learning models, prioritizing data privacy. However, the inherent heterogeneity in FL networks, arising from variations in data distribution, size, and device capabilities, poses challenges in user federation. Recognizing this, Personalized Federated Learning (PFL) emphasizes tailoring learning processes to individual data profiles. In this paper, we address the complexity of clustering users in PFL, especially in dynamic networks, by introducing a dynamic Upper Confidence Bound (dUCB) algorithm inspired by the multi-armed bandit (MAB) approach. The dUCB algorithm ensures that new users can effectively find the best cluster for their data distribution by balancing exploration and exploitation. The performance of our algorithm is evaluated in various cases, showing its effectiveness in handling dynamic federated learning scenarios.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 29 Oct 2023.

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12 of 20 references.

References · 12

  1. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  2. 2021Towards Personalized Federated Learningcited by 464
  3. 2020Adaptive Personalized Federated Learningcited by 358
  4. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  5. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  6. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  7. 2020LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasetscited by 60
  8. 2021Federated Multi-Armed Banditscited by 59
  9. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 57
  10. 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
  11. 2022MIX-MAB: Reinforcement Learning-based Resource Allocation Algorithm for LoRaWANcited by 3
  12. 2022A Deep Reinforcement Learning based Approach for NOMA-based Random Access Network with Truncated Channel Inversion Power Controlcited by 1

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Datacited by 3