mlsanityData to 7 Oct 2026

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients

arXiv 2409.20329 ↗First posted 30 Sep 2024cs.LG cs.CRcited by 5

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Abstract (arXiv)

Federated learning (FL) is an appealing paradigm that allows a group of machines (a.k.a. clients) to learn collectively while keeping their data local. However, due to the heterogeneity between the clients' data distributions, the model obtained through the use of FL algorithms may perform poorly on some client's data. Personalization addresses this issue by enabling each client to have a different model tailored to their own data while simultaneously benefiting from the other clients' data. We consider an FL setting where some clients can be adversarial, and we derive conditions under which full collaboration fails. Specifically, we analyze the generalization performance of an interpolated personalized FL framework in the presence of adversarial clients, and we precisely characterize situations when full collaboration performs strictly worse than fine-tuned personalization. Our analysis determines how much we should scale down the level of collaboration, according to data heterogeneity and the tolerable fraction of adversarial clients. We support our findings with empirical results on mean estimation and binary classification problems, considering synthetic and benchmark image classification datasets.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 25 of its 36 references that are in the map. First posted on arXiv 30 Sep 2024.

Builds on

25 of 36 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  4. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  5. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  6. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  7. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  8. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  9. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  10. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  11. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  12. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
13 more
  1. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  2. 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 125
  3. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 122
  4. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 86
  5. 2016Decentralized Collaborative Learning of Personalized Models over Networkscited by 73
  6. 2023Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneitycited by 64
  7. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
  8. 2023Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneitycited by 25
  9. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
  10. 2023Optimizing the Collaboration Structure in Cross-Silo Federated Learningcited by 19
  11. 2023Provably Personalized and Robust Federated Learningcited by 11
  12. 2020Robustness and Personalization in Federated Learning: A Unified Approach via Regularizationcited by 8
  13. 2023Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneitycited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learningcited by 2
  2. 2025Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneitycited by 1
  3. 2025Lorica: A Synergistic Fine-Tuning Framework for Advancing Personalized Adversarial Robustnesscited by 0
  4. 2025Robust and Efficient Collaborative Learningcited by 0
  5. 2026Byzantine-Robust and Communication-Efficient Distributed Training: Compressive and Cyclic Gradient Codingcited by 0