Federated Learning Approaches for Data Privacy Preservation

Abstract

Federated Learning (FL) enables collaborative model training across distributed clients without centralizing raw data, thereby reducing exposure to privacy risks such as data exfiltration, membership inference, and gradient leakage. This article synthesizes privacy-preserving approaches in FL—including secure aggregation, differential privacy, trusted execution environments, split/hybrid learning, and personalized optimization—while examining their impacts on utility, communication cost, and system robustness under non-IID data. We propose a layered privacy blueprint (protocol, algorithmic, and system layers) and outline deployment guidelines for regulated domains (healthcare, finance, public sector). A comparative analysis highlights privacy–utility trade-offs and recommends combining secure aggregation with calibrated differential privacy and adaptive client sampling to meet compliance without unduly sacrificing accuracy.