mlsanityData to 14 Oct 2026

Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning

arXiv 2605.11815 ↗First posted 12 May 2026cs.LGcited by 0

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Abstract (arXiv)

Hierarchical federated learning (HFL) leverages edge servers for partial aggregation in edge computing. Yet existing FL methods lack mechanisms for jointly optimizing cluster assignment and client selection under data heterogeneity. This paper proposes Fed-BAC, which integrates additive cluster personalization with a two-level bandit framework: contextual bandits at the cloud learn server-to-cluster assignments, while Thompson Sampling at each edge server identifies high-contributing clients. The additive decomposition enables the sharing of knowledge between groups through a globally aggregated network, while cluster-specific networks capture distribution variations. Across three classification benchmarks (CIFAR-10, SVHN, Fashion-MNIST) under moderate (α = 0.5) and severe (α = 0.1) Dirichlet non-IID partitioning, Fed-BAC achieves distributed accuracy gains of up to +35.5pp over HierFAVG and +8.4pp over IFCA, while requiring only 80% client participation, converging 1.5 to 4.8× faster depending on dataset and accuracy target, and improving cross-server fairness. These gains are further validated at 5× deployment scale on CIFAR-10. The advantage of Fed-BAC increases with heterogeneity severity, confirming that additive cluster personalization becomes increasingly valuable as data distributions diverge.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 37 references that are in the map. First posted on arXiv 12 May 2026.

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References · 28

  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. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  6. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
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  9. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  10. 2021Towards Personalized Federated Learningcited by 464
  11. 2017A Tutorial on Thompson Samplingcited by 433
  12. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
16 more
  1. 2020Adaptive Personalized Federated Learningcited by 358
  2. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  3. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 305
  4. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  5. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  6. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 187
  7. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  8. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 57
  9. 2020Bandit-based Communication-Efficient Client Selection Strategies for Federated Learningcited by 29
  10. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  11. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  12. 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
  13. 2022Fast Heterogeneous Federated Learning with Hybrid Client Selectioncited by 8
  14. 2025Interaction-Aware Gaussian Weighting for Clustered Federated Learningcited by 5
  15. 2025Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Datacited by 1
  16. 2025Clustered Federated Learning with Hierarchical Knowledge Distillationcited by 1

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