Privacy preservation using optimized Federated Learning: A critical survey

Abstract

FL is a futuristic research topic that enables cross-sectoral training in ML systems in various organizations with some privacy restrictions. This review article establishes the extensive review of FL with different privacy-preserving techniques and the obstacles involved in the existing privacy-preserving model. This review is initiated by providing the background of FL and provides an overview of the technical details of the component involved in FL. Then it provides a brief review of the around 75 articles related to privacy-preserving in the FL-enabled techniques. Compared to the other survey articles this presented review article provides a brief analysis of the different privacy terms utilized in FL. The categorization of the privacy preservation models in FL highlights the significance of the model and the obstacles that limit the application of the particular privacy preservation model in real-time application. Further, this review articles ensure the details about the year of publishing, performance metrics analyzed in different articles along with their achievements. The limitation experienced in each category of the privacy-preserving technique is elaborated briefly, which assists future researchers to explore more privacy-preserving models in FL.