Federated learning method for dynamic weight adjustment: An in-depth analysis of FedDyn algorithm

Abstract

With the gradual enrichment of the application of deep learning in daily life, distributed machine learning has played a greater role in daily life, among which federated learning and its optimization algorithm FedDyn algorithm have begun to receive widespread attention. After introducing federated learning and traditional algorithms, this paper starts with the operation mode and framework of the FedDyn algorithm, takes the combination method of port selection strategy and global distribution as an example, analyzes the characteristics of some links in the operation process and uses the innovation points of the FedDyn algorithm : Dynamic weight adjustment is the entry point, which illustrates the advantages of the FedDyn algorithm in current use, and also points out some of the current problems. Finally, from the aspect of market application, the current application status of the algorithm is introduced from the aspects of personalized recommendation, medical treatment and finance, and the future development of the algorithm is prospected.