Optimal Transport Barycentric Aggregation for Byzantine-Resilient Federated Learning

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Abstract
Federated learning (FL) has emerged as a promising solution to enable distributed learning without sharing sensitive data. However, FL is vulnerable to data poisoning attacks, where malicious clients inject malicious data during training to compromise the global model. Existing FL defenses suffer from the assumptions of independent and identically distributed (IID) model updates, asymptotic optimal error rate bounds, and strong convexity in the optimization problem. Hence, we propose a novel framework called Federated Learning Optimal Transport (FLOT) that leverages the Wasserstein barycentric technique to obtain a global model from a set of locally trained non-IID models on client devices. In addition, we introduce a loss function-based rejection (LFR) mechanism to suppress malicious updates and a dynamic weighting scheme to optimize the Wasserstein barycentric aggregation function. We provide the theoretical proof of the Byzantine resilience and convergence of FLOT to highlight its efficacy. We evaluate FLOT on four benchmark datasets: GTSRB, KBTS, CIFAR10, and EMNIST. The experimental results underscore the practical significance of FLOT as an effective defense mechanism against data poisoning attacks in FL while maintaining high accuracy and scalability. Also, we observe that FLOT serves as a robust client selection technique under no attack, which demonstrates its effectiveness.