Web: http://arxiv.org/abs/2110.03681

Jan. 31, 2022, 2:11 a.m. | Kai Yue, Richeng Jin, Ryan Pilgrim, Chau-Wai Wong, Dror Baron, Huaiyu Dai

cs.LG updates on arXiv.org arxiv.org

Federated learning (FL) is a privacy-preserving paradigm where multiple
participants jointly solve a machine learning problem without sharing raw data.
Unlike traditional distributed learning, a unique characteristic of FL is
statistical heterogeneity, namely, data distributions across participants are
different from each other. Meanwhile, recent advances in the interpretation of
neural networks have seen a wide use of neural tangent kernels (NTKs) for
convergence analyses. In this paper, we propose a novel FL paradigm empowered
by the NTK framework. The paradigm …

arxiv federated learning kernel learning neural

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