This repo contains demo code for the following paper:
Qinqing Zheng, Shuxiao Chen, Qi Long, Weijie J. Su. Federated f-Differential Privacy. AISTATS 2021. [arXiv:2102.11158]
Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data. In this paper, we introduce federated f-differential privacy, a new notion specifically tailored to the federated setting, based on the framework of Gaussian differential privacy. Federated f-differential privacy operates on record level: it provides the privacy guarantee on each individual record of one client’s data against adversaries. We then propose a generic private federated learning framework PriFedSync that accommodates a large family of state-of-the-art FL algorithms, which provably achieves federated f-differential privacy.
code: code to run private FL training for non-iid MNIST and non-iid CIFAR. One might need to change the data path indata.py.config: sample config files for the setup used in our experiments.