Secure Aggregation in Federated Learning is not Private: Leaking User Data at Large Scale through Model Modification. (arXiv:2303.12233v1 [cs.LG])

Security and privacy are important concerns in machine learning. End user
devices often contain a wealth of data and this information is sensitive and
should not be shared with servers or enterprises. As a result, federated
learning was introduced to enable machine learning over large decentralized
datasets while promising privacy by eliminating the need for data sharing.
However, prior work has shown that shared gradients often contain private
information and attackers can gain knowledge either through malicious
modification of the …

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