EIFFeL: Ensuring Integrity for Federated Learning. (arXiv:2112.12727v2 [cs.CR] UPDATED)

Federated learning (FL) enables clients to collaborate with a server to train
a machine learning model. To ensure privacy, the server performs secure
aggregation of updates from the clients. Unfortunately, this prevents
verification of the well-formedness (integrity) of the updates as the updates
are masked. Consequently, malformed updates designed to poison the model can be
injected without detection. In this paper, we formalize the problem of ensuring
\textit{both} update privacy and integrity in FL and present a new system,
\textsf{EIFFeL}, …

federated learning integrity