Self Organizing Federated Learning Over Wireless Networks: A Socially Aware Clustering Approach
Latif U. Khan, Madyan Alsenwi, Zhu Han, Choong Seon Hong
Abstract
The significant proliferation of the Internet of Things (IoT) devices generates an enormous amount of data. Availability of such a large amount of data offers opportunities for using machine learning to enable intelligence in numerous applications. However, centralized machine learning schemes are based on migrating the data from devices to a centralized location for training. Such migration of data from user devices to a centralized location suffers from significant privacy concerns. To cope with this privacy preservation challenge, federated learning is a viable solution which enables learning in a distributed manner without migrating the data from devices to a centralized location. In this paper, we propose a novel federated learning scheme that offers federated learning without using centralized cloud server. First, we present a clustering algorithm based on social awareness which is followed by cluster head selection. Second, we formulate an optimization problem to minimize global federated learning time. Due to the NP-hard nature of the formulated optimization problem, we propose a heuristic algorithm to optimize the global federated learning time. Finally, we present numerical results to validate our proposed algorithm.