Journal
2020 34TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2020)
Volume -, Issue -, Pages 453-458Publisher
IEEE
DOI: 10.1109/icoin48656.2020.9016505
Keywords
Federated learning; device to device communication; machine learning; resource optimization
Funding
- National Research Foundation of Korea(NRF) - Korea government(MSIT) [NRF-2017R1A2A2A05000995]
- Institute of Information communications Technology Planning Evaluation (IITP) - Korea goverment(MSIT) [2019-0-01287]
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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.
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