Architectural blueprint for heterogeneity-resilient federated learning
Conference paper
Bashir, S., Dagiuklas, T., Kassai, K. and Iqbal, M. (2024). Architectural blueprint for heterogeneity-resilient federated learning. IET 6G and Future Networks Conference (IET 6G 2024). London, UK 24 - 25 Jun 2024 Institution of Engineering and Technology (IET). https://doi.org/10.1049/icp.2024.2236
Authors | Bashir, S., Dagiuklas, T., Kassai, K. and Iqbal, M. |
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Type | Conference paper |
Abstract | This paper proposes a novel three-tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy-preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates the architecture’s capability to manage non-IID data sets more effectively than traditional federated learning models. Additionally, the paper highlights the potential of this innovative approach to significantly improve model accuracy, reduce communication overhead, and facilitate broader adoption of federated learning technologies. |
Keywords | federated learning; edge computing; data privacy |
Year | 2024 |
Publisher | Institution of Engineering and Technology (IET) |
Digital Object Identifier (DOI) | https://doi.org/10.1049/icp.2024.2236 |
Web address (URL) | https://digital-library.theiet.org/content/conferences/10.1049/icp.2024.2236 |
Accepted author manuscript | License File Access Level Open |
Publication dates | |
19 Jul 2024 | |
Publication process dates | |
Accepted | 23 May 2024 |
Deposited | 19 Jul 2024 |
Web address (URL) of conference proceedings | https://digital-library.theiet.org/content/conferences/cp879 |
https://openresearch.lsbu.ac.uk/item/97v8y
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