Joint Privacy Enhancement and Quantization in Federated Learning. (arXiv:2208.10888v1 [cs.LG])

Federated learning (FL) is an emerging paradigm for training machine learning
models using possibly private data available at edge devices. The distributed
operation of FL gives rise to challenges that are not encountered in
centralized machine learning, including the need to preserve the privacy of the
local datasets, and the communication load due to the repeated exchange of
updated models. These challenges are often tackled individually via techniques
that induce some distortion on the updated models, e.g., local differential
privacy …

federated learning lg privacy