Feb. 15, 2024, 5:42 a.m. | Yousef Alsenani, Rahul Mishra, Khaled R. Ahmed, Atta Ur Rahman

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09095v1 Announce Type: new
Abstract: In recent years, federated learning (FL) has emerged as a promising technique for training machine learning models in a decentralized manner while also preserving data privacy. The non-independent and identically distributed (non-i.i.d.) nature of client data, coupled with constraints on client or edge devices, presents significant challenges in FL. Furthermore, learning across a high number of communication rounds can be risky and potentially unsafe for model exploitation. Traditional FL approaches may suffer from these challenges. …

arxiv constraints cs.cr cs.lg distillation federated learning knowledge type

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