mlsanityData to 14 Oct 2026

Provably Personalized and Robust Federated Learning

arXiv 2306.08393 ↗First posted 14 Jun 2023cs.LG cs.DCcited by 11

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Abstract (arXiv)

Identifying clients with similar objectives and learning a model-per-cluster is an intuitive and interpretable approach to personalization in federated learning. However, doing so with provable and optimal guarantees has remained an open challenge. We formalize this problem as a stochastic optimization problem, achieving optimal convergence rates for a large class of loss functions. We propose simple iterative algorithms which identify clusters of similar clients and train a personalized model-per-cluster, using local client gradients and flexible constraints on the clusters. The convergence rates of our algorithms asymptotically match those obtained if we knew the true underlying clustering of the clients and are provably robust in the Byzantine setting where some fraction of the clients are malicious.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 14 Jun 2023.

Builds on

24 of 33 references.

References · 24

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  4. 2017Federated Multi-Task Learningcited by 672
  5. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  6. 2018Taskonomy: Disentangling Task Transfer Learningcited by 634
  7. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  8. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  9. 2021Towards Personalized Federated Learningcited by 464
  10. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 329
  11. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  12. 2019Robust Aggregation for Federated Learningcited by 317
12 more
  1. 2019Lower Bounds for Non-Convex Stochastic Optimizationcited by 307
  2. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 288
  3. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  4. 2020Survey of Personalization Techniques for Federated Learningcited by 145
  5. 2018Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptroncited by 143
  6. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
  7. 2020Learning from History for Byzantine Robust Optimizationcited by 137
  8. 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 128
  9. 2021Personalized Federated Learning through Local Memorizationcited by 57
  10. 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
  11. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  12. 2022Personalized Federated Learning via Heterogeneous Modular Networkscited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 8; 2025: 2; 2026: 1.

11 papers.

  1. 2024Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learningcited by 9
  2. 2024CoBo: Collaborative Learning via Bilevel Optimizationcited by 5
  3. 2024Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clientscited by 5
  4. 2024Distributionally Robust Clustered Federated Learning: A Case Study in Healthcarecited by 2
  5. 2025Adaptive collaboration for online personalized distributed learning with heterogeneous clientscited by 2
  6. 2024Collaborative and Efficient Personalization with Mixtures of Adaptorscited by 1
  7. 2025CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddingscited by 1
  8. 2024Defection-Free Collaboration between Competitors in a Learning Systemcited by 0
  9. 2024Fast convergence of a Federated Expectation-Maximization Algorithmcited by 0
  10. 2024Your Data, My Model: Learning Who Really Helps in Federated Learningcited by 0
  11. 2026SP-CACW: Convergence-Aware Client Weighting for Selfish Personalized Learningcited by 0