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pro vyhledávání: '"Reshef, Roie"'
Autor:
Reshef, Roie, Levy, Kfir Y.
This paper addresses the challenge of preserving privacy in Federated Learning (FL) within centralized systems, focusing on both trusted and untrusted server scenarios. We analyze this setting within the Stochastic Convex Optimization (SCO) framework
Externí odkaz:
http://arxiv.org/abs/2407.12396
Neural networks are susceptible to privacy attacks. To date, no verifier can reason about the privacy of individuals participating in the training set. We propose a new privacy property, called local differential classification privacy (LDCP), extend
Externí odkaz:
http://arxiv.org/abs/2310.20299