Secure Aggregation for Federated Learning in Flower. (arXiv:2205.06117v1 [cs.LG])

Federated Learning (FL) allows parties to learn a shared prediction model by
delegating the training computation to clients and aggregating all the
separately trained models on the server. To prevent private information being
inferred from local models, Secure Aggregation (SA) protocols are used to
ensure that the server is unable to inspect individual trained models as it
aggregates them. However, current implementations of SA in FL frameworks have
limitations, including vulnerability to client dropouts or configuration
difficulties.

In this paper, …

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