Self Organizing Federated Learning Over Wireless Networks: A Socially Aware Clustering Approach
Latif Ullah Khan, Madyan Alsenwi, Zhu Han, Choong Seon Hong · 2020
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.