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Personalized Multi-tier Federated Learning

arXiv 2407.14251 ↗First posted 19 Jul 2024cs.LG cs.AI math.OCcited by 0

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Abstract (arXiv)

The key challenge of personalized federated learning (PerFL) is to capture the statistical heterogeneity properties of data with inexpensive communications and gain customized performance for participating devices. To address these, we introduced personalized federated learning in multi-tier architecture (PerMFL) to obtain optimized and personalized local models when there are known team structures across devices. We provide theoretical guarantees of PerMFL, which offers linear convergence rates for smooth strongly convex problems and sub-linear convergence rates for smooth non-convex problems. We conduct numerical experiments demonstrating the robust empirical performance of PerMFL, outperforming the state-of-the-art in multiple personalized federated learning tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 41 references that are in the map. First posted on arXiv 19 Jul 2024.

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References · 33

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  9. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  10. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  11. 2021Towards Personalized Federated Learningcited by 462
  12. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
21 more
  1. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  2. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  3. 2018Don't Use Large Mini-Batches, Use Local SGDcited by 201
  4. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  5. 2022Federated Learning with Partial Model Personalizationcited by 114
  6. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  7. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  8. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  9. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  10. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  11. 2022Adaptive and Robust Multi-Task Learningcited by 33
  12. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  13. 2021Scalable federated machine learning with FEDncited by 16
  14. 2020Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systemscited by 14
  15. 2021FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learningcited by 12
  16. 2020Enhancing Privacy via Hierarchical Federated Learningcited by 10
  17. 2021Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Datacited by 10
  18. 2022Straggler-Resilient Personalized Federated Learningcited by 9
  19. 2018The Internet of Things, Fog and Cloud Continuum: Integration and Challengescited by 6
  20. 2021Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioningcited by 6
  21. 2022Personalized Federated Learning with Multiple Known Clusterscited by 3

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