Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model. (arXiv:2304.05516v1 [cs.CR])

Federated Learning, as a popular paradigm for collaborative training, is
vulnerable against privacy attacks. Different privacy levels regarding users'
attitudes need to be satisfied locally, while a strict privacy guarantee for
the global model is also required centrally. Personalized Local Differential
Privacy (PLDP) is suitable for preserving users' varying local privacy, yet
only provides a central privacy guarantee equivalent to the worst-case local
privacy level. Thus, achieving strong central privacy as well as personalized
local privacy with a utility-promising model …

amplification attacks case differential privacy echo federated learning global guarantee local locally paradigm popular privacy private problem shuffle training utility vulnerable