New Paper Accepted

Our paper Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation, joint work with Mounssif Krouka, Anis Elgabli, and Mehdi Bennis has been accepted in 2021 IEEE Global Communications Conference (GLOBECOM).

Abstract:

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 communication 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 outperforms the digital implementation in terms of communication efficiency, especially as the number of agents grows large.

Chaouki ben Issaid
Chaouki ben Issaid
Postdoctoral Fellow Researcher

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