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Research Area: Machine Learning
The ongoing deployments of the Internet of Things (IoT)-based smart applications are spurring the adoption of machine learning as a key technology enabler. To overcome the privacy and overhead challenges of centralized machine learning, there has been significant recent interest in the concept of federated learning. Federated learning offers on-device machine learning without the need to transfer end-device data to a third party location. However, federated learning has robustness concerns because it might stop working due to a failure of the aggregation server (e.g., due to a malicious attack or physical defect). Furthermore, federated learning over IoT networks requires a significant amount of communication resources for training. To cope with these issues, we propose a novel framework of dispersed federated learning (DFL) that is based on true decentralization. We opine that DFL will serve as a practical implementation of federated learning for various IoT-based smart applications such as smart industries and intelligent transportation systems. First, the fundamentals of the DFL are presented. Second, a taxonomy is devised with a qualitative analysis of various DFL schemes. Third, a DFL framework for IoT networks is proposed with a matching theory-based solution. Finally, an outlook on future research directions is presented.
Keywords:
Dispersed Federated Learning
Internet of Things
Deep Learning
Machine Learning
Author(s) Name: Latif U. Khan; Walid Saad; Zhu Han; Choong Seon Hong
Journal name: IEEE Wireless Communications
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/MWC.011.2100003
Volume Information: ( Volume: 28, Issue: 5, October 2021) Page(s): 192 - 198
