Federated Learning for Privacy-Preserving Data Mining

Federated learning (FL) is an emerging approach that enables collaborative machine learning while preserving data privacy. Privacy has become a critical issue in the big data era, particularly in data mining applications that handle sensitive data. Classical machine learning necessitates gathering participant data for training, which could result in the illegal collection of personal information. FL removes the requirement for transferring raw data to a central server by training models across decentralized devices that hold local data samples. This paper discusses the methodology, implementation, and evaluation of federated learning as it relates to privacy-preserving data mining. The outcomes of our experiments show how FL can be used to achieve high model accuracy while protecting user privacy. The paper’s conclusion outlines potential avenues for future research and enhancements to federated learning frameworks in order to strengthen privacy protections in data mining projects.