A Nesterov-Accelerated Byzantine-Robust Federated Learning

This paper presents a federated learning algorithm that combines Nesterov acceleration with Byzantine-robust aggregation to improve convergence speed while maintaining security against malicious participants in distributed training. The approach addresses a key challenge in enterprise federated learning: enabling fast, accurate model training across multiple organizations while protecting against compromised or adversarial data sources.

This paper presents a federated learning algorithm that combines Nesterov acceleration with Byzantine-robust aggregation to improve convergence speed while maintaining security against malicious participants in distributed training. The approach addresses a key challenge in enterprise federated learning: enabling fast, accurate model training across multiple organizations while protecting against compromised or adversarial data sources. The technique is particularly relevant for regulated industries where data cannot be centralized but robust collaboration is required.

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