Entropy to Mitigate Non-IID Data Problem on Federated Learning for the Edge Intelligence Environment-Reference-Cited by-同舟云学术

Entropy to Mitigate Non-IID Data Problem on Federated Learning for the Edge Intelligence Environment

Published:2023 Issue: Volume:11 Page:78845-78857
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Orlandi Fernanda C.¹^ORCID,Dos Anjos Julio C. S.²^ORCID,Leithardt Valderi R. Q.³^ORCID,De Paz Santana Juan Francisco⁴^ORCID,Geyer Claudio F. R.¹^ORCID

Affiliation:

1. Federal University of Rio Grande do Sul, Institute of Informatics, UFRGS/PPGC, Porto Alegre, Rio Grande do Sul, Brazil

2. Federal University of Ceará, Graduate Program in Teleinformatics Engineering (PPGETI/UFC), Center of Technology, Campus of Pici, Fortaleza, Ceará, Brazil

3. VALORIZA-Polytechnic Institute of Portalegre, Portalegre, Portugal

4. Expert Systems and Applications Laboratory (ESALAB), Faculty of Science, University of Salamanca, Salamanca, Spain

Funder

CAPES

Spanish Agencia Estatal de Investigación through the Project Monitoring and Tracking Systems for the Improvement of Intelligent Mobility and Behavior Analysis

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10192897.pdf?arnumber=10192897

Reference41 articles.

1. Semisupervised Distributed Learning With Non-IID Data for AIoT Service Platform

2. Reschedule Gradients: Temporal Non-IID Resilient Federated Learning

3. Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing

4. FedMDS: An Efficient Model Discrepancy-Aware Semi-Asynchronous Clustered Federated Learning Framework

5. A Survey on Distributed Machine Learning

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