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On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learning. (arXiv:2308.14606v2 [cs.LG] UPDATED)
cs.CR updates on arXiv.org arxiv.org
This paper jointly considers privacy preservation and Byzantine-robustness in
decentralized learning. In a decentralized network, honest-but-curious agents
faithfully follow the prescribed algorithm, but expect to infer their
neighbors' private data from messages received during the learning process,
while dishonest-and-Byzantine agents disobey the prescribed algorithm, and
deliberately disseminate wrong messages to their neighbors so as to bias the
learning process. For this novel setting, we investigate a generic
privacy-preserving and Byzantine-robust decentralized stochastic gradient
descent (SGD) framework, in which Gaussian noise …
algorithm data decentralized expect messages network preservation privacy private private data process robustness