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Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
Chuan Guo · Kamalika Chaudhuri · Pierre Stock · Michael Rabbat

Thu Jul 27 01:30 PM -- 03:00 PM (PDT) @ Exhibit Hall 1 #111

In private federated learning (FL), a server aggregates differentially private updates from a large number of clients in order to train a machine learning model. The main challenge in this setting is balancing privacy with both classification accuracy of the learnt model as well as the number of bits communicated between the clients and server. Prior work has achieved a good trade-off by designing a privacy-aware compression mechanism, called the minimum variance unbiased (MVU) mechanism, that numerically solves an optimization problem to determine the parameters of the mechanism. This paper builds upon it by introducing a new interpolation procedure in the numerical design process that allows for a far more efficient privacy analysis. The result is the new Interpolated MVU mechanism that is more scalable, has a better privacy-utility trade-off, and provides SOTA results on communication-efficient private FL on a variety of datasets.

Author Information

Chuan Guo (Meta AI)
Kamalika Chaudhuri (UCSD, Meta AI Research, and FAIR)
Pierre Stock (Facebook)
Michael Rabbat (McGill University)

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