Fortifying Multi-Agent Architectures in Smart Manufacturing: Leveraging Federated Learning and Blockchain for Security and Resilience

This work presents a blockchain-based communication architecture for multi-agent smart manufacturing systems, enhanced with federated learning to improve security, data privacy, and scalability. Comparative evaluation with traditional centralized systems across various configurations reveals that our approach achieves up to 12 % higher model accuracy and 15 % greater security, particularly in large-scale deployments, with scalability scores surpassing those of centralized systems by 18 % as agent numbers increase. Although initial latency and energy consumption are higher, these factors improve significantly as the system scales, indicating suitability for complex manufacturing networks. This research underscores the potential of combining federated learning and blockchain to enhance resilience, security, and efficiency in Industry 4.0 smart manufacturing systems.

Authors

Institutions

Publication Details

Published
2025-01-10
DOI
https://doi.org/10.1109/ccnc54725.2025.10975963
Citations
3
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
9.74
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fortifying Multi-Agent Architectures in Smart Manufacturing: Leveraging Federated Learning and Blockchain for Security and Resilience

Petro M. Tshakwanda, Michael Devetsikiotis, Henok Berhanu Tsegaye, Raddad Almaayn
3 citations
Blockchain Technology Applications and Security
9.74
article

Fortifying Multi-Agent Architectures in Smart Manufacturing: Leveraging Federated Learning and Blockchain for Security and Resilience

Petro M. Tshakwanda, Michael Devetsikiotis, Henok Berhanu Tsegaye, Raddad Almaayn
article en
3 citations

Abstract

This work presents a blockchain-based communication architecture for multi-agent smart manufacturing systems, enhanced with federated learning to improve security, data privacy, and scalability. Comparative evaluation with traditional centralized systems across various configurations reveals that our approach achieves up to 12 % higher model accuracy and 15 % greater security, particularly in large-scale deployments, with scalability scores surpassing those of centralized systems by 18 % as agent numbers increase. Although initial latency and energy consumption are higher, these factors improve significantly as the system scales, indicating suitability for complex manufacturing networks. This research underscores the potential of combining federated learning and blockchain to enhance resilience, security, and efficiency in Industry 4.0 smart manufacturing systems.

University of New Mexico (US), University of Trento (IT)
Openalex Percentile: Top 2%
Blockchain Technology Applications and Security
9.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.