CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness
This work addresses privacy concerns in distributed machine learning for applications like healthcare or finance, but it is incremental as it builds on existing federated learning and differential privacy techniques.
The paper tackles the challenge of balancing privacy, communication efficiency, and model accuracy in federated learning by introducing CorBin-FL, a mechanism using correlated binary stochastic quantization to achieve differential privacy, which outperforms existing methods like Gaussian and Laplacian mechanisms on MNIST and CIFAR10 datasets under equal privacy budgets.
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. Extensive experiments on MNIST and CIFAR10 datasets demonstrate that our mechanisms outperform existing differentially private FL mechanisms, including Gaussian and Laplacian mechanisms, in terms of model accuracy under equal PLDP privacy budgets.