A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed

A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed

Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge devices of smart infrastructures to train a collaborative model independently while keeping data localized. FL algorithms, encompassing a well-structured average of the training parameters (e.g., the w...

Full description

Bibliographic Details
Main Authors: Tarek Berghout, Toufik Bentrcia, Mohamed Amine Ferrag, Mohamed Benbouzid
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Mathematics
Subjects:
federated learning
federated transfer learning
heterogeneous systems
non identical independent data
recursive least squares
Online Access:https://www.mdpi.com/2227-7390/10/19/3528

Internet

https://www.mdpi.com/2227-7390/10/19/3528

Similar Items