ARES: Adaptive Resource-Aware Split Learning for Internet of Things

Samikwa, Eric; Di Maio, Antonio; Braun, Torsten (2022). ARES: Adaptive Resource-Aware Split Learning for Internet of Things. Computer Networks, 218, p. 109380. Elsevier 10.1016/j.comnet.2022.109380

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Distributed training of Machine Learning models in edge Internet of Things (IoT) environments is challenging because of three main points. First, resource-constrained devices have large training times and limited energy budget. Second, resource heterogeneity of IoT devices slows down the training of the global model due to the presence of slower devices (stragglers). Finally, varying operational conditions, such as network bandwidth, and computing resources, significantly affect training time and energy consumption. Recent studies have proposed Split Learning (SL) for distributed model training with limited resources but its efficient implementation on the resource-constrained and decentralized heterogeneous IoT devices remains minimally explored. We propose Adaptive REsource-aware Splitlearning (ARES), a scheme for efficient model training in IoT systems. ARES accelerates local training in resource-constrained devices and minimizes the effect of stragglers on the training through device-targeted split points while accounting for time-varying network throughput and computing resources. ARES takes into account application constraints to mitigate training optimization tradeoffs in terms of energy consumption and training time. We evaluate ARES prototype on a real testbed comprising heterogeneous IoT devices running a widely-adopted deep neural network and dataset. Results show that ARES accelerates model training on IoT devices by up to 48% and minimizes the energy consumption by up to 61.4% compared to Federated Learning (FL) and classic SL, without sacrificing the model convergence and accuracy

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF)
08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS)

UniBE Contributor:

Samikwa, Eric, Di Maio, Antonio, Braun, Torsten

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics

ISSN:

1389-1286

Publisher:

Elsevier

Language:

English

Submitter:

Dimitrios Xenakis

Date Deposited:

26 Sep 2022 12:41

Last Modified:

20 Sep 2024 18:52

Publisher DOI:

10.1016/j.comnet.2022.109380

Related URLs:

Uncontrolled Keywords:

Split learning; Internet of things; Distributed machine learning; Federated learning; Edge computing

BORIS DOI:

10.48350/173081

URI:

https://boris.unibe.ch/id/eprint/173081

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