Federated Learning with Differential Privacy: A Comprehensive Framework for Privacy-Preserving Distributed Machine Learning

Abstract

Overview: Federated Learning (FL) is a type of machine learning in which multiple clients use their own data to train a model. This does not require sending the raw data from each client to a central server. However, FL is currently very vulnerable to several types of attacks, including inference, model inversion, and membership inference. In this study, we have implemented a comprehensive experimental framework for analysing FL performance using standard FL aggregation protocols FedAvg, FedProx, and SCAFFOLD in conjunction with Differential Privacy (DP) mechanisms; specifically, the Gaussian noise mechanism with Rényi Differential Privacy (RDP) accountants. Our experiments all used K = 100 simulated clients, with heterogeneous data distribution across the entire federation (non-IID), and the respective clean datasets used were MNIST and CIFAR-10. For example, in our IID scenarios, our DP-FedAvg protocol achieved an accuracy of 84.9% on MNIST with ε = 1.0 and δ = 10^-5 (i.e., no privacy guarantees and 92.6% reuse of the same parameters). This corresponds to a 7.7-percentage-point trade-off in accuracy for provable privacy. In our non-IID scenario, we achieved an accuracy of 72.8% for the exact same privacy parameters and an LDA αᴸᴸᴸ = 0.5. We systematically analyse how various combinations of clipping norms on gradients, noise multipliers, and client participation rates converged. All results have been reported as-is, along with the associated standard deviation across each of the five independent sample runs that we performed using different random seed values. Finally, our framework will assist practitioners deploying privacy-friendly FL within either healthcare, banking, or Internet of Things environments.