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Efficient and Secure Federated Learning for Financial Applications. (arXiv:2303.08355v1 [cs.LG])
Web: http://arxiv.org/abs/2303.08355
March 16, 2023, 1:10 a.m. | Tao Liu, Zhi Wang, Hui He, Wei Shi, Liangliang Lin, Wei Shi, Ran An, Chenhao Li
cs.CR updates on arXiv.org arxiv.org
The conventional machine learning (ML) and deep learning approaches need to
share customers' sensitive information with an external credit bureau to
generate a prediction model that opens the door to privacy leakage. This
leakage risk makes financial companies face an enormous challenge in their
cooperation. Federated learning is a machine learning setting that can protect
data privacy, but the high communication cost is often the bottleneck of the
federated systems, especially for large neural networks. Limiting the number
and size …
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