Advances in federal learning technology research

Houji Jin

doi:10.1117/12.2675227

25 May 2023 Advances in federal learning technology research

Houji Jin

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360G (2023) https://doi.org/10.1117/12.2675227
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

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.

Citation Download Citation

Houji Jin "Advances in federal learning technology research", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360G (25 May 2023); https://doi.org/10.1117/12.2675227

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available