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Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph. (arXiv:2210.00325v1 [cs.CR])
Oct. 4, 2022, 1:20 a.m. | Yang Lu, Zhengxin Yu, Neeraj Suri
cs.CR updates on arXiv.org arxiv.org
Establishing how a set of learners can provide privacy-preserving federated
learning in a fully decentralized (peer-to-peer, no coordinator) manner is an
open problem. We propose the first privacy-preserving consensus-based algorithm
for the distributed learners to achieve decentralized global model aggregation
in an environment of high mobility, where the communication graph between the
learners may vary between successive rounds of model aggregation. In
particular, in each round of global model aggregation, the Metropolis-Hastings
method is applied to update the weighted adjacency …
More from arxiv.org / cs.CR updates on arXiv.org
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