Randomized Quantization is All You Need for Differential Privacy in Federated Learning. (arXiv:2306.11913v1 [cs.LG])

Federated learning (FL) is a common and practical framework for learning a
machine model in a decentralized fashion. A primary motivation behind this
decentralized approach is data privacy, ensuring that the learner never sees
the data of each local source itself. Federated learning then comes with two
majors challenges: one is handling potentially complex model updates between a
server and a large number of data sources; the other is that de-centralization
may, in fact, be insufficient for privacy, as the …

data data privacy decentralized decentralized approach differential privacy fashion federated learning framework local machine motivation privacy sees