Personalized, robust federated learning with fed+
Publication Type
Book Chapter
Publication Date
7-2022
Abstract
Fed+ is a unified family of methods designed to better accommodate the real-world characteristics found in federated learning training, such as the lack of IID data across parties and the need for robustness to outliers. Fed+ does not require all parties to reach a consensus, allowing each party to train local, personalized models through a form of regularization while benefiting from the federation to improve accuracy and performance. The methods included in the Fed+ family are shown to be provably convergent. Experiments indicate that Fed+ outperform other methods when data is not IID, and the robust versions of Fed+ outperform other methods in the presence of outliers.
Discipline
Numerical Analysis and Scientific Computing
Research Areas
Intelligent Systems and Optimization
Publication
Federated learning: A comprehensive overview of methods and applications
Editor
LUDWIG, Heiko; BARACALDO, Nathalie
First Page
99
Last Page
123
ISBN
9783030968960
Identifier
10.1007/978-3-030-96896-0_5
Publisher
Springer International Publishing
City or Country
Cham
Citation
YU, Pengqian; KUNDU, Achintya; WYNTER, Laura; and LIM, Shiau Hong.
Personalized, robust federated learning with fed+. (2022). Federated learning: A comprehensive overview of methods and applications. 99-123.
Available at: https://ink.library.smu.edu.sg/sis_research/10348
Additional URL
https://doi.org/10.1007/978-3-030-96896-0_5