mlsanityData to 14 Oct 2026

Straggler-Resilient Personalized Federated Learning

arXiv 2206.02078 ↗First posted 5 Jun 2022cs.LG cs.DCcited by 9

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Abstract (arXiv)

Federated Learning is an emerging learning paradigm that allows training models from samples distributed across a large network of clients while respecting privacy and communication restrictions. Despite its success, federated learning faces several challenges related to its decentralized nature. In this work, we develop a novel algorithmic procedure with theoretical speedup guarantees that simultaneously handles two of these hurdles, namely (i) data heterogeneity, i.e., data distributions can vary substantially across clients, and (ii) system heterogeneity, i.e., the computational power of the clients could differ significantly. Our method relies on ideas from representation learning theory to find a global common representation using all clients' data and learn a user-specific set of parameters leading to a personalized solution for each client. Furthermore, our method mitigates the effects of stragglers by adaptively selecting clients based on their computational characteristics and statistical significance, thus achieving, for the first time, near optimal sample complexity and provable logarithmic speedup. Experimental results support our theoretical findings showing the superiority of our method over alternative personalized federated schemes in system and data heterogeneous environments.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 35 of its 49 references that are in the map. First posted on arXiv 5 Jun 2022.

Builds on

35 of 49 references.

References · 35

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  5. 2017Federated Multi-Task Learningcited by 672
  6. 2018Local SGD Converges Fast and Communicates Littlecited by 605
  7. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  8. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  9. 2020Adaptive Personalized Federated Learningcited by 358
  10. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  11. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  12. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
23 more
  1. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 312
  2. 2019Asynchronous Federated Optimizationcited by 267
  3. 2020Federated Learning of a Mixture of Global and Local Modelscited by 210
  4. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 175
  5. 2020Provable Meta-Learning of Linear Representationscited by 161
  6. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 160
  7. 2019Federated Evaluation of On-device Personalizationcited by 147
  8. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  9. 2019On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithmscited by 132
  10. 2021Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradientscited by 123
  11. 2022Federated Learning with Partial Model Personalizationcited by 114
  12. 2020Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneitycited by 56
  13. 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 56
  14. 2021Anarchic Federated Learningcited by 42
  15. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  16. 2021Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfercited by 23
  17. 2022FedShuffle: Recipes for Better Use of Local Work in Federated Learningcited by 21
  18. 2019Fundamental Limits of Approximate Gradient Codingcited by 15
  19. 2019Robust and Communication-Efficient Collaborative Learningcited by 14
  20. 2021Private Multi-Task Learning: Formulation and Applications to Federated Learningcited by 11
  21. 2021Secure Aggregation for Buffered Asynchronous Federated Learningcited by 10
  22. 2022Partial Model Averaging in Federated Learning: Performance Guarantees and Benefitscited by 8
  23. 2018Coded Sparse Matrix Multiplicationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 1; 2026: 2.

9 papers.

  1. 2023Federated Minimax Optimization with Client Heterogeneitycited by 8
  2. 2022PersA-FL: Personalized Asynchronous Federated Learningcited by 5
  3. 2024On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representationscited by 4
  4. 2023PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientscited by 3
  5. 2023Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologiescited by 3
  6. 2026Self-Creating Random Walks for Decentralized Learning under Pac-Man Attackscited by 2
  7. 2025Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Frameworkcited by 1
  8. 2026On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systemscited by 1
  9. 2024Personalized Multi-tier Federated Learningcited by 0