ScionFL: Secure Quantized Aggregation for Federated Learning. (arXiv:2210.07376v1 [cs.CR])

Privacy concerns in federated learning (FL) are commonly addressed with
secure aggregation schemes that prevent a central party from observing
plaintext client updates. However, most such schemes neglect orthogonal FL
research that aims at reducing communication between clients and the aggregator
and is instrumental in facilitating cross-device FL with thousands and even
millions of (mobile) participants. In particular, quantization techniques can
typically reduce client-server communication by a factor of 32x.

In this paper, we unite both research directions by introducing …

aggregation arxiv federated learning