July 26, 2021

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Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization. (arXiv:2008.01558v2 [cs.LG] UPDATED)

Federated learning (FL) enables distributed agents to collaboratively learn a
centralized model without sharing their raw data with each other. However, data
locality does not provide sufficient privacy protection, and it is desirable to
facilitate FL with rigorous differential privacy (DP) guarantee. Existing DP
mechanisms would introduce random noise with magnitude proportional to the
model size, which can be quite large in deep neural networks. In this paper, we
propose a new FL framework with sparsification-amplified privacy. Our approach
integrates random sparsification with gradient perturbation on each agent to
amplify privacy guarantee. Since sparsification would increase the number of
communication rounds required to achieve a certain target accuracy, which is
unfavorable for DP guarantee, we further introduce acceleration techniques to
help reduce the privacy cost. We rigorously analyze the convergence of our
approach and utilize Renyi DP to tightly account the end-to-end DP guarantee.
Extensive experiments on benchmark datasets validate that our approach
outperforms previous differentially-private FL approaches in both privacy
guarantee and communication efficiency.