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Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM. (arXiv:2207.10226v2 [cs.LG] UPDATED)
July 25, 2022, 1:20 a.m. | Chulin Xie, Pin-Yu Chen, Ce Zhang, Bo Li
cs.CR updates on arXiv.org arxiv.org
Federated learning (FL) enables distributed devices to jointly train a shared
model while keeping the training data local. Different from the horizontal FL
(HFL) setting where each client has partial data samples, vertical FL (VFL),
which allows each client to collect partial features, has attracted intensive
research efforts recently. In this paper, we identified two challenges that
state-of-the-art VFL frameworks are facing: (1) some works directly average the
learned feature embeddings and therefore might lose the unique properties of
each …
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