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

Gleb Radchenko; Victoria Fill

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

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

Topics: AI-enabled Edge Cloud; Multi-access Edge Cloud

In Proceedings of the 14th International Conference on Cloud Computing and Services Science CLOSER - Volume 1, 311-318, 2024 , Angers, France

Authors: Gleb Radchenko ¹ and Victoria Fill ²

Affiliations: ¹ Silicon Austria Labs, Sandgasse 34, 8010 Graz, Austria ; ² FH Joanneum, Alte Poststraße 149, 8020 Graz, Austria

Keyword(s): Edge Computing, Fog Computing, Machine Learning, IoT.

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.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 3.143.229.85

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

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 - CLOSER; ISBN 978-989-758-701-6; ISSN 2184-5042, SciTePress, pages 311-318. DOI: 10.5220/0012728500003711

@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 - CLOSER},
year={2024},
pages={311-318},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012728500003711},
isbn={978-989-758-701-6},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Cloud Computing and Services Science - CLOSER
TI - Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices
SN - 978-989-758-701-6
IS - 2184-5042
AU - Radchenko, G.
AU - Fill, V.
PY - 2024
SP - 311
EP - 318
DO - 10.5220/0012728500003711
PB - SciTePress