mlsanityData to 14 Oct 2026

DART: A Server-side Plug-in for Resource-efficient Robust Federated Learning

arXiv 2508.17381 ↗First posted 24 Aug 2025cs.LGcited by 0

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Abstract (arXiv)

Federated learning (FL) emerged as a popular distributed algorithm to train machine learning models on edge devices while preserving data privacy. However, FL systems face challenges due to client-side computational constraints and from a lack of robustness to naturally occurring common corruptions such as noise, blur, and weather effects. Existing robust training methods are computationally expensive and unsuitable for resource-constrained clients. We propose a novel data-agnostic robust training (DART) plug-in that can be deployed in any FL system to enhance robustness at zero client overhead. DART operates at the server-side and does not require private data access, ensuring seamless integration in existing FL systems. Extensive experiments showcase DART's ability to enhance robustness of state-of-the-art FL systems, establishing it as a practical and scalable solution for real-world robust FL deployment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 40 of its 54 references that are in the map. First posted on arXiv 24 Aug 2025.

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References · 40

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