Trading Off Privacy, Utility and Efficiency in Federated Learning. (arXiv:2209.00230v1 [cs.LG])

Federated learning (FL) enables participating parties to collaboratively
build a global model with boosted utility without disclosing private data
information. Appropriate protection mechanisms have to be adopted to fulfill
the opposing requirements in preserving \textit{privacy} and maintaining high
model \textit{utility}. In addition, it is a mandate for a federated learning
system to achieve high \textit{efficiency} in order to enable large-scale model
training and deployment. We propose a unified federated learning framework that
reconciles horizontal and vertical federated learning. Based on …

federated learning privacy trading utility