Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices

Gleb Radchenko, Victoria Fill

2024

Abstract

Edge IoT devices, once seen as low-power units with limited processing, have evolved with introduction of FPGAs and AI accelerators, significantly boosting their computational power for edge AI. This leads to new challenges in optimizing AI for energy and network resource constraints in edge computing. Our study examines methods for distributed data processing with AI-enabled edge devices to improve collaborative learning. We focus on the challenge of assessing confidence in learning outcomes amid data variability faced by agents. To address this issue, we investigate the application of Bayesian neural networks, proposing a novel approach to manage uncertainty in distributed learning environments.

Paper Citation

in Harvard Style

Radchenko G. and Fill V. (2024). Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices. In Proceedings of the 14th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER; ISBN 978-989-758-701-6, SciTePress, pages 311-318. DOI: 10.5220/0012728500003711

in Bibtex Style

@conference{closer24,
author={Gleb Radchenko and Victoria Fill},
title={Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices},
booktitle={Proceedings of the 14th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER},
year={2024},
pages={311-318},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012728500003711},
isbn={978-989-758-701-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER
TI - Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices
SN - 978-989-758-701-6
AU - Radchenko G.
AU - Fill V.
PY - 2024
SP - 311
EP - 318
DO - 10.5220/0012728500003711
PB - SciTePress