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

Fuente: arXiv
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Main Authors: Elkeshawy, Ali, Ghosh, Ibrahim Al, Fares, Haifa, Nafkha, Amor
Format: Preprint
Published: 2025
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author Elkeshawy, Ali
Ghosh, Ibrahim Al
Fares, Haifa
Nafkha, Amor
author_facet Elkeshawy, Ali
Ghosh, Ibrahim Al
Fares, Haifa
Nafkha, Amor
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2503_13513
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks
Elkeshawy, Ali
Ghosh, Ibrahim Al
Fares, Haifa
Nafkha, Amor
Signal Processing
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.
title Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks
topic Signal Processing
url https://arxiv.org/abs/2503.13513