FedSV: Byzantine-Robust Federated Learning via Shapley Value

2024-06-09

ICC 2024 - IEEE International Conference on Communications

AI Summary

To address the degradation of global model performance in federated learning caused by malicious client poisoning attacks, this paper proposes FedSV, a robust aggregation mechanism based on Shapley values. FedSV’s core innovation lies in the first extension of Shapley value computation to estimate *multi-group collaborative contributions*, dynamically quantifying each client’s marginal contribution across diverse client subsets—enabling fine-grained, context-aware detection of anomalous behavior. By jointly modeling accuracy-based marginal gains and optimizing federated aggregation, FedSV is specifically designed for cross-silo settings. Extensive experiments on MNIST under various Byzantine attack scenarios demonstrate that FedSV improves global model accuracy by up to 12.7% and achieves an F1-score exceeding 0.93 for malicious client identification—substantially enhancing system robustness against adversarial clients.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Adversarial Agents
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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 [1]. 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 conribution 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 conribution 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.

Problem

• Byzantine attacks in Federated Learning
• Robust identification of malicious clients
• Shapley Value for user contribution measurement

Innovation

• Shapley Value for robustness
• Federated Learning defense
• Malicious client identification