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Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks by Wang, Su; Hosseinalipour, Seyyedali; Aggarwal, Vaneet; Brinton, Christopher G.; Love, David J.; Su, Weifeng; Chiang, Mung is a scholarly article available to read on EtoBox.

What is Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks about?

Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation, treating edge/fog devices and other infrastructure participating in ML as separate processing elements. Consequently, FL has been vulnerable to several dimensions of network heterogeneity, such as varying computation capabilities, communication resources, data qualities, and privacy demands. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-to-server (D2S) interactions. Through D2D and D2S cooperation, CFL counteracts network heterogeneity in edge/fog networks through enabling a model/data/resource pooling mechanism, which will yield substantial improvements in ML model training quality and network resource consumption. We propose a set of core methodologies that form the foundation of D2D and D2S cooperation and present preliminary experiments that demonstrate their benefits. We also discuss new FL functionalities enabled by this cooperative framework such as the integrat

Author

Wang, Su; Hosseinalipour, Seyyedali; Aggarwal, Vaneet; Brinton, Christopher G.; Love, David J.; Su, Weifeng; Chiang, Mung

Published

2023

Language

EN