Enhanced Security and Privacy via Fragmented Federated Learning. (arXiv:2207.05978v2 [cs.CR] UPDATED)

In federated learning (FL), a set of participants share updates computed on
their local data with an aggregator server that combines updates into a global
model. However, reconciling accuracy with privacy and security is a challenge
to FL. On the one hand, good updates sent by honest participants may reveal
their private local information, whereas poisoned updates sent by malicious
participants may compromise the model's availability and/or integrity. On the
other hand, enhancing privacy via update distortion damages accuracy, whereas …

federated learning privacy security