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CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness by Salehi, Hojat Allah; Mia, Md Jueal; Pradhan, S. Sandeep; Amini, M. Hadi; Shirani, Farhad is a scholarly article available to read on EtoBox.

What is CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness about?

Federated learning (FL) has emerged as a promising framework for distributed machine learning. It enables collaborative learning among multiple clients, utilizing distributed data and computing resources. However, FL faces challenges in balancing privacy guarantees, communication efficiency, and overall model accuracy. In this work, we introduce CorBin-FL, a privacy mechanism that uses correlated binary stochastic quantization to achieve differential privacy while maintaining overall model accuracy. The approach uses secure multi-party computation techniques to enable clients to perform correlated quantization of their local model updates without compromising individual privacy. We provide theoretical analysis showing that CorBin-FL achieves parameter-level local differential privacy (PLDP), and that it asymptotically optimizes the privacy-utility trade-off between the mean square error utility measure and the PLDP privacy measure. We further propose AugCorBin-FL, an extension that, in addition to PLDP, achieves user-level and sample-level central differential privacy guarantees. For both mechanisms, we derive bounds on privacy parameters and mean squared error performance measures

Author

Salehi, Hojat Allah; Mia, Md Jueal; Pradhan, S. Sandeep; Amini, M. Hadi; Shirani, Farhad

Published

2024

Language

EN