HSADR: A New Highly Secure Aggregation and Dropout-Resilient Federated Learning Scheme for Radio Access Networks With Edge Computing Systems

Open radio access network (ORAN) plays a critical role in modern communication process. The structure that individual devices connect each other via ORAN turns to be a part of smart city. Incorporating with the concept Internet of Things (IoT), cloud-edge-client architecture has been accepted to discuss artificial intelligence (AI) coordinating functions in ORAN. Considering the security for ORAN in critical infrastructure, federated learning (FL) is an effective way to protect the original data on individual devices. However, recent schemes failed to support enough security features. To tackle the problem, we present a new highly secure aggregation and dropout-resilient FL scheme called HSADR which incorporates consortium blockchain and differential privacy to maintain the security environment. Second, we prove that the aggregation process reaches the IND-CCA2 security level, which is the first scheme to complete this goal. Last, experiments show that HSADR withstands common test aspects.

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Publication Details

Journal
IEEE Transactions on Green Communications and Networking
Published
2024-08-12
DOI
https://doi.org/10.1109/tgcn.2024.3441532
Citations
5
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
1.74

Funders

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article

HSADR: A New Highly Secure Aggregation and Dropout-Resilient Federated Learning Scheme for Radio Access Networks With Edge Computing Systems

Pandi Vijayakumar, Jingwei Li, Fan Wu, Brij B. Gupta et al.
5 citations
IEEE Transactions on Green Communications and Networking
Privacy-Preserving Technologies in Data
1.74
article

HSADR: A New Highly Secure Aggregation and Dropout-Resilient Federated Learning Scheme for Radio Access Networks With Edge Computing Systems

Pandi Vijayakumar, Jingwei Li, Fan Wu, Brij B. Gupta, Varsha Arya, Xiong Li
article en
5 citations

Abstract

Open radio access network (ORAN) plays a critical role in modern communication process. The structure that individual devices connect each other via ORAN turns to be a part of smart city. Incorporating with the concept Internet of Things (IoT), cloud-edge-client architecture has been accepted to discuss artificial intelligence (AI) coordinating functions in ORAN. Considering the security for ORAN in critical infrastructure, federated learning (FL) is an effective way to protect the original data on individual devices. However, recent schemes failed to support enough security features. To tackle the problem, we present a new highly secure aggregation and dropout-resilient FL scheme called HSADR which incorporates consortium blockchain and differential privacy to maintain the security environment. Second, we prove that the aggregation process reaches the IND-CCA2 security level, which is the first scheme to complete this goal. Last, experiments show that HSADR withstands common test aspects.

IEEE Transactions on Green Communications and NetworkingVol. 8(3)
Asia University (TW), University of Electronic Science and Technology of China (CN), Anna University, Chennai (IN)
National Natural Science Foundation of China, National Key Research and Development Program of China, Chengdu Science and Technology Program
Openalex Percentile: Top 13%
Privacy-Preserving Technologies in Data
1.74
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HSADR: A New Highly Secure Aggregation and Dropout-Resilient Federated Learning Scheme for Radio Access Networks With Edge Computing Systems — Pandi Vijayakumar, Jingwei Li, et al. · IEEE Transactions on Green Communications and Networking (2024) | TGRS Research Map | TGRS