Secure Aggregation of Semi-Honest Clients and Servers in Federated Learning with Secret-Shared Homomorphism. (arXiv:2212.11394v1 [cs.CR])

Privacy-preserving distributed machine learning has been recognized as one of
the most promising paradigms for the collaboration of multiple or many clients
who cannot disclose their local data -- neither the training data set nor the
model parameters. One popular approach to preserving the confidentiality of
local data is homomorphic encryption (HE): the clients send encrypted model
parameters to the server, which aggregates the models into a global model and
broadcasts it to the clients. Evidently, the security of the …

clients federated learning secret servers