Communication Dans Un Congrès Année : 2025

Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks

Résumé

Grant-free random access in massive machine-type communications enables low-latency connectivity with minimal signaling. However, sporadic device activation requires efficient device activity detection. We propose a federated learning-based device activity detection approach, leveraging distributed training to enhance security and privacy while maintaining low computational complexity. Compared to existing methods, our solution achieves competitive detection performance, addressing scalability and security challenges in mMTC networks.

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hal-05082461 , version 1 (23-05-2025)

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  • HAL Id : hal-05082461 , version 1

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Ali Elkeshawy, Ibrahim Al Ghosh, Haïfa Farès, Amor Nafkha. Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks. EuCNC & 6G Summit, IEEE Communications Society (ComSoc); The European Association for Signal Processing (EURASIP); The European Association on Antennas and Propagation (EurAAP), Jun 2025, Poznan, Poland. ⟨hal-05082461⟩
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