- Title
- Resource optimized federated learning-enabled cognitive internet of things for smart industries
- Creator
- Khan, Latif; Alsenwi, Madyan; Yaqoob, Ibrar; Imran, Muhammad; Han, Zhu; Hong, Choong
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184182
- Identifier
- vital:16440
- Identifier
-
https://doi.org/10.1109/ACCESS.2020.3023940
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- Leveraging the cognitive Internet of things (C-IoT), emerging computing technologies, and machine learning schemes for industries can assist in streamlining manufacturing processes, revolutionizing operational analytics, and maintaining factory efficiency. However, further adoption of centralized machine learning in industries seems to be restricted due to data privacy issues. Federated learning has the potential to bring about predictive features in industrial systems without leaking private information. However, its implementation involves key challenges including resource optimization, robustness, and security. In this article, we propose a novel dispersed federated learning (DFL) framework to provide resource optimization, whereby distributed fashion of learning offers robustness. We formulate an integer linear optimization problem to minimize the overall federated learning cost for the DFL framework. To solve the formulated problem, first, we decompose it into two sub-problems: association and resource allocation problem. Second, we relax the association and resource allocation sub-problems to make them convex optimization problems. Later, we use the rounding technique to obtain binary association and resource allocation variables. Our proposed algorithm works in an iterative manner by fixing one problem variable (for example, association) and compute the other (for example, resource allocation). The iterative algorithm continues until convergence of the formulated cost optimization problem. Furthermore, we compare the proposed DFL with two schemes; namely, random resource allocation and random association. Numerical results show the superiority of the proposed DFL scheme. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 8, no. (2020), p. 168854-168864
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2020 IEEE
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and Computing Sciences; Cognitive Internet of Things; Convex optimization; Federated learning; Smart industry
- Full Text
- Reviewed
- Funder
- This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government, Ministry of Science and ICT (MSIT), South Korea, Evolvable Deep Learning Model Generation Platform for Edge Computing under Grant 2019-0-01287, and in part by MSIT through the Grand Information Technology Research Center Support Program supervised by IITP under Grant IITP-2020-2015-0-00742.
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