Feb. 8, 2024, 5:10 a.m. | Meiying Zhang Huan Zhao Sheldon Ebron Kan Yang

cs.CR updates on arXiv.org arxiv.org

The performance of clients in Federated Learning (FL) can vary due to various reasons. Assessing the contributions of each client is crucial for client selection and compensation. It is challenging because clients often have non-independent and identically distributed (non-iid) data, leading to potentially noisy or divergent updates. The risk of malicious clients amplifies the challenge especially when there's no access to clients' local data or a benchmark root dataset. In this paper, we introduce a novel method called Fair, Robust, …

can client clients compensation cs.ai cs.cr cs.dc cs.lg data distributed divergent evaluation fair federated federated learning noisy non performance risk updates

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