Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-offs in Distributed Mean Estimation

Neural Information Processing Systems 

Privacy and communication constraints are two major bottlenecks in federated learning (FL) and analytics (FA).

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