M. Krouka, A. Elgabli, C. b. Issaid and M. Bennis, "Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation," 2021 IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685045
Communication-efficient split learning based on analog communication and over the air aggregation
|Author:||Krouka, Mounssif1; Elgabli, Anis1; Issaid, Chaouki ben1;|
1Centre for Wireless Communications (CWC), University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022022320601
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-02-23
Split-learning (SL) has recently gained popularity due to its inherent privacy-preserving capabilities and ability to enable collaborative inference for devices with limited computational power. Standard SL algorithms assume an ideal underlying digital communication system and ignore the problem of scarce communication bandwidth. However, for a large number of agents, limited bandwidth resources, and time-varying commu-nication channels, the communication bandwidth can become the bottleneck. To address this challenge, in this work, we propose a novel SL framework to solve the remote inference problem that introduces an additional layer at the agent side and constrains the choices of the weights and the biases to ensure over the air aggregation. Hence, the proposed approach maintains constant communication cost with respect to the number of agents enabling remote inference under limited bandwidth. Numerical results show that our proposed algorithm significantly outper-forms the digital implementation in terms of communication-efficiency” especially as the number of agents grows large.
|Pages:||1 - 6|
2021 IEEE Global Communications Conference (GLOBECOM) : Proceedings
IEEE Global Communications Conferenc
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is supported by Academy of Finland 6G Flagship (grant no. 318927) and project SMARTER, projects EU-ICT IntellIoT and EUCHIS-TERA LearningEdge, and CONNECT, Infotech-NOOR, and NEGEIN.
|EU Grant Number:||
(957218) IntellIoT - Intelligent, distributed, human-centered and trustworthy IoT environments
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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