Federated Learning Methods for Privacy-Preserving Collaborative Machine Learning

Federated learning is a cutting-edge data solution that protects privacy as well as a fresh machine learning model developed on collaborative data sets. A major concern privacy protection aspect of machine learning has proven quite successful. The PFMLP framework, which is based on federated learning as well as collaborative machine learning, is presented in this paper for multi-party safeguarding privacy machine learning. All learning partners ought to send only homomorphic encrypted gradients, according to the basic idea. According to experiments, the PFMLP-trained model almost always achieves the same accuracy, with a variance of less than 1.%. The upcoming privacy-preserving data technology and a fresh category of distributed learning models, federated learning, were both invented by Google. In this research, we examine distributed ML applied to dispersed data sets and federated learning as an option for privacy-preserving data access. Additionally, a federated learning architecture that protects privacy is presented.