Original paper
Toward Communication-Efficient Federated Learning in the Internet of Things With Edge Computing
Volume: 7, Issue: 11, Pages: 11053 - 11067
Published: May 16, 2020
Abstract
Federated learning is an emerging concept that trains the machine learning models with the local distributed data sets, without sending the raw data to the data center. But, in the Internet of Things (IoT) where the wireless network resource is constrained, the key problem of federated learning is the communication overhead for parameter synchronization, which wastes bandwidth, increases training time, and even impacts the model accuracy....
Paper Details
Title
Toward Communication-Efficient Federated Learning in the Internet of Things With Edge Computing
Published Date
May 16, 2020
Volume
7
Issue
11
Pages
11053 - 11067