Advanced Privacy-Preserving Mechanisms in Federated Learning: A Comprehensive Literature Review, Methodological Analysis, and Future Directions

Federated Learning (FL) represents a groundbreaking approach to distributed machine learning which allows model training across various decentralized datasets while keeping data storage confined to local location points. The privacy benefits of FL work against diverse attacks such as model inversion attacks and both membership inference attacks and participant collusion threats. This research examines all key privacy-preserving methods which developed for FL including Differential Privacy (DP), Homomorphic Encryption (HE), Knowledge Distillation (KD), Dataset Distillation (DD), and Blockchain Integration. Our study employs methodological comparison to analyze these privacy techniques and their FL workflow integration as well as their privacy-scale-efficiency trade-offs. The combination of DP with HE methods together with integrating KD with dataset distillation denotes strong potential in enhancing security features of federated learning systems.