Privacy-Preserving Federated Learning Platform using Differential Privacy And Secure Aggregation

Federated learning enables decentralized machine learning model training on distributed client devices whilepreserving raw data privacy. However, vulnerability to model inversion and gradient leakage attacks remains acritical security threat during parameter communication. This paper proposes a comprehensive Privacy-Preserving Federated Learning (PPFL) framework integrating Differential Privacy (DP) via per-sample gradient clipping and Gaussian noise addition with cryptographic Secure Aggregation(SecAgg). In the proposed platform, participating edge clients retain their datasets locally and execute private model training. Local updates are privatized using Rényi Differential Privacy (RDP) accounting before undergoing double-masked secret sharing for secure server-side aggregation. Experimental validation using Python PyTorch,Flower FLwr, and Opacus frameworks demonstrates robust privacy protection with minimal utility degradation across multi-round distributed training.