FLEDGY: Federated Learning for Edge Devices Accommodating GPU Diversity

We propose a federated learning framework de-signed to effectively utilize diverse GPU architectures in edge devices. It offers wider compatibility with various GPU platforms than conventional approaches which tend to focus on CUDA-based systems for enhancing the feasibility and cost-efficiency of m...

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Bibliographic Details
Published in2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) pp. 1528 - 1529
Main Authors Prabono, Aria Ghora, Goh, Yang Fan, Kobayashi, Mei
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.07.2024
Subjects
Complexity theory
Computer architecture
edge computing
Federated learning
Graphics processing units
Libraries
machine learning
Software
Tensors
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Summary:We propose a federated learning framework de-signed to effectively utilize diverse GPU architectures in edge devices. It offers wider compatibility with various GPU platforms than conventional approaches which tend to focus on CUDA-based systems for enhancing the feasibility and cost-efficiency of machine learning in edge computing scenarios. Our framework is structured for straightforward deployment, addressing the complexities of initial federated learning project setups. We also built tensor manipulation library from scratch as the core of this framework. This contribution extends the reach of federated learning, enabling more flexible and inclusive approaches in distributed machine learning environments.
ISSN:2836-3795
DOI:10.1109/COMPSAC61105.2024.00222