mlsanityData to 14 Oct 2026

FedSV: Byzantine-Robust Federated Learning via Shapley Value

arXiv 2502.17526 ↗First posted 24 Feb 2025cs.LG cs.CR cs.GTcited by 6

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Abstract (arXiv)

In Federated Learning (FL), several clients jointly learn a machine learning model: each client maintains a local model for its local learning dataset, while a master server maintains a global model by aggregating the local models of the client devices. However, the repetitive communication between server and clients leaves room for attacks aimed at compromising the integrity of the global model, causing errors in its targeted predictions. In response to such threats on FL, various defense measures have been proposed in the literature. In this paper, we present a powerful defense against malicious clients in FL, called FedSV, using the Shapley Value (SV), which has been proposed recently to measure user contribution in FL by computing the marginal increase of average accuracy of the model due to the addition of local data of a user. Our approach makes the identification of malicious clients more robust, since during the learning phase, it estimates the contribution of each client according to the different groups to which the target client belongs. FedSV's effectiveness is demonstrated by extensive experiments on MNIST datasets in a cross-silo context under various attacks.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 24 Feb 2025.

Builds on

15 of 24 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  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. 2019Robust Aggregation for Federated Learningcited by 317
  5. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 288
  6. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 225
  7. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 149
  8. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 118
  9. 2021Sampling Permutations for Shapley Value Estimationcited by 102
  10. 2018Federated Learning for Keyword Spottingcited by 95
  11. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 88
  12. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
3 more
  1. 2022FLCert: Provably Secure Federated Learning against Poisoning Attackscited by 30
  2. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  3. 2023FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregationcited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025A Survey on Foundation Models for Personalized Federated Intelligencecited by 3
  2. 2025Detect & Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learningcited by 1
  3. 2025On the Fragility of Contribution Score Computation in Federated Learningcited by 1
  4. 2026Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMscited by 1
  5. 2026Beyond performance-wise Contribution Evaluation in Federated Learningcited by 0
  6. 2026Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theorycited by 0