Parallel and Memory-Efficient Distributed Edge Learning in B5G IoT Networks | IEEE Journals & Magazine | IEEE Xplore

Parallel and Memory-Efficient Distributed Edge Learning in B5G IoT Networks


Abstract:

Nowadays we are witnessing rapid development of the Internet of Things (IoT), machine learning, and cellular network technologies. They are key components to promote wire...Show More

Abstract:

Nowadays we are witnessing rapid development of the Internet of Things (IoT), machine learning, and cellular network technologies. They are key components to promote wireless networks beyond 5G (B5G). The plenty of data generated from numerous IoT devices, such as smart sensors and mobile devices, can be utilised to train intelligent models. But it still remains a challenge to efficiently utilise IoT networks and edge in B5G to conduct model training. In this paper, we propose a parallel training method which uses operators as scheduling units during training task assignment. Besides, we discuss a pebble-game-based memory-efficient optimisation in training. Experiments based on various real world network architectures show the flexibility of our proposed method and good performance compared with state of the art.
Published in: IEEE Journal of Selected Topics in Signal Processing ( Volume: 17, Issue: 1, January 2023)
Page(s): 222 - 233
Date of Publication: 21 November 2022

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