Advances in federal learning technology research
Houji Jin · 2023
Along with the boom in data rights and the increasing number of responses to protect data privacy, scholars have begun to study the "data isolation" phenomenon while ensuring data privacy and security. Users upload source data to a high computing power cloud server for centralised training required by traditional machine learning algorithms, which leads to uncontrolled data theft and sensitive data leakage problems. By removing data barriers and collaborating on modelling utilizing data from several clients, federated learning has become a successful remedy for this issue. In this presentation, the history of federated learning is discussed, federated learning’s precise defining categories is clarified, and its technological evolution is examined. How well training frameworks for federated learning, including the FedAvg, MOON, and Fedprox algorithms perform is assessed too. Finally, the current problems of federated learning algorithms and the future directions of development are also illustrated objectively.