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image of Privacy-Preserving Sensing in MIMO Networks Using Federated Learning

Abstract

Introduction

The rapid growth of Internet of Things (IoT) devices and advancements in wireless communication have driven the adoption of multiple-input multiple-output (MIMO) networks for intelligent sensing. However, traditional centralized data processing raises significant privacy concerns, necessitating privacy-preserving alternatives.

Methods

This study introduces a federated learning (FL)-based distributed sensing architecture for MIMO networks. Each node locally trains a model using its received signal data and transmits only the model updates to a central server. A novel model aggregation strategy has been developed to account for spatial diversity and varying channel conditions in MIMO systems.

Results

Simulation results reveal that the proposed FL-MIMO framework achieves sensing accuracy comparable to centralized methods while maintaining raw data privacy. The approach exhibits robustness to non-independent and identically distributed (non-IID) data and asynchronous communication, with negligible performance degradation.

Discussion

The findings demonstrate the feasibility of applying federated learning to MIMO-based sensing, addressing key challenges such as communication overhead, model convergence, and security against adversarial threats. The method effectively mitigates privacy risks without compromising sensing performance.

Conclusion

The proposed FL-MIMO framework provides a practical and secure solution for privacy-preserving sensing in smart environments. By balancing efficiency and privacy, it facilitates scalable and trustworthy deployment of intelligent sensing applications in real-world MIMO networks.

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2025-07-30
2025-12-14

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/content/journals/swcc/10.2174/0122103279378837250717041135
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Privacy-Preserving Sensing in MIMO Networks Using Federated Learning
Bentham Science Publishers ; https://doi.org/10.2174/0122103279378837250717041135
/content/journals/swcc/10.2174/0122103279378837250717041135
/content/journals/swcc/10.2174/0122103279378837250717041135
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  • Article Type:     Research Article
Keywords: spatial diversity ; Federated learning ; smart cities ; secure multi-party computation (SMPC) ; secure aggregation ; multi-input multi-output (MIMO) networks ; communication overhead reduction ; privacy-preserving sensing ; Internet of things ; 5G/6G wireless networks

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