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Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. (arXiv:2106.09779v6 [cs.LG] UPDATED)
Nov. 1, 2022, 1:20 a.m. | Andrew Lowy, Meisam Razaviyayn
cs.CR updates on arXiv.org arxiv.org
This paper studies federated learning (FL) -- especially cross-silo FL --
with data from people who do not trust the server or other silos. In this
setting, each silo (e.g. hospital) has data from different people (e.g.
patients) and must maintain the privacy of each person's data (e.g. medical
record), even if the server or other silos act as adversarial eavesdroppers.
This requirement motivates the study of Inter-Silo Record-Level Differential
Privacy (ISRL-DP), which requires silo $i$'s communications to satisfy
record-level …
More from arxiv.org / cs.CR updates on arXiv.org
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