Research on privacy protection and performance optimization of 6G communication network based on the fusion of federated learning and edge computing

The 6G networks have made the need to ensure privacy of the user data as well as high-performance communication urgent. The centralized machine learning models possess several issues such as high latency, privacy breach and non-scaled performance especially applied on dynamic distributed systems such as Human Activity Recognition (HAR). To address these shortcomings, the paper suggests a new framework would integrate Federated Learning (FL) with Edge Intelligence (EI) and Differential Privacy (DP). The proposed model allows decentralized activation of activity recognition models in edge devices themselves, thereby eliminating the need to transfer raw data to the central servers. Differential Privacy boasts an extra level of privacy since it adds a controlled noise to the gradient updates hence protection against data reconstruction attacks. The model was tested on the UCI-HAR data with 5-fold cross-validation and achieved impressive. It had an average accuracy of 99.25, an F1-score of 0.9871, and a Kappa score of 0.9909, which is a strong predictive performance and consistency. Also, the False Negative Rate (FNR) and False Positive Rate (FPR) decreased to 0.0027 and 0.0115, respectively, which indicates the accuracy of the model. Unlike other baseline models like SVM and CNN-BiLSTM, the given system significantly reduces the latency and communication overhead at a high accuracy and privacy guarantees. It can thus be used well in real-time smart cities, healthcare, and industrial IoT. In general, this research advances safe and effective edge-based AI schemes towards 6G systems. Federated Learning, Edge Intelligence, and Differentiated privacy combined to create secure, decentralized activity recognition is an innovation. It is a very accurate method that keeps the user privacy, reduces latency and lets the real-time processing operate without showing raw data beyond the 6G network.

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Publication Details

Journal
Discover Artificial Intelligence
Published
2025-12-13
DOI
https://doi.org/10.1007/s44163-025-00687-x
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
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article

Research on privacy protection and performance optimization of 6G communication network based on the fusion of federated learning and edge computing

Haifeng Zhao
Discover Artificial Intelligence
Privacy-Preserving Technologies in Data
article

Research on privacy protection and performance optimization of 6G communication network based on the fusion of federated learning and edge computing

Haifeng Zhao
article en

Abstract

The 6G networks have made the need to ensure privacy of the user data as well as high-performance communication urgent. The centralized machine learning models possess several issues such as high latency, privacy breach and non-scaled performance especially applied on dynamic distributed systems such as Human Activity Recognition (HAR). To address these shortcomings, the paper suggests a new framework would integrate Federated Learning (FL) with Edge Intelligence (EI) and Differential Privacy (DP). The proposed model allows decentralized activation of activity recognition models in edge devices themselves, thereby eliminating the need to transfer raw data to the central servers. Differential Privacy boasts an extra level of privacy since it adds a controlled noise to the gradient updates hence protection against data reconstruction attacks. The model was tested on the UCI-HAR data with 5-fold cross-validation and achieved impressive. It had an average accuracy of 99.25, an F1-score of 0.9871, and a Kappa score of 0.9909, which is a strong predictive performance and consistency. Also, the False Negative Rate (FNR) and False Positive Rate (FPR) decreased to 0.0027 and 0.0115, respectively, which indicates the accuracy of the model. Unlike other baseline models like SVM and CNN-BiLSTM, the given system significantly reduces the latency and communication overhead at a high accuracy and privacy guarantees. It can thus be used well in real-time smart cities, healthcare, and industrial IoT. In general, this research advances safe and effective edge-based AI schemes towards 6G systems. Federated Learning, Edge Intelligence, and Differentiated privacy combined to create secure, decentralized activity recognition is an innovation. It is a very accurate method that keeps the user privacy, reduces latency and lets the real-time processing operate without showing raw data beyond the 6G network.

Discover Artificial IntelligenceVol. 6(1)
Aviation Industry Corporation of China (China) (CN)
Openalex Percentile: Top 79%
Privacy-Preserving Technologies in Data
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