Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911020420694016 |
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| author | Xu, Kaidi Zhou, Shenglong Li, Geoffrey Ye |
| author_facet | Xu, Kaidi Zhou, Shenglong Li, Geoffrey Ye |
| contents | Future wireless networks are expected to be AI-empowered, making their performance highly dependent on the quality of training datasets. However, physical-layer entities often observe only partial wireless environments characterized by different power delay profiles. Federated learning is capable of addressing this limited observability, but often struggles with data heterogeneity. To tackle this challenge, we propose a neural collapse (NC) inspired deep supervised federated learning (NCDSFL) algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19476 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems Xu, Kaidi Zhou, Shenglong Li, Geoffrey Ye Signal Processing Future wireless networks are expected to be AI-empowered, making their performance highly dependent on the quality of training datasets. However, physical-layer entities often observe only partial wireless environments characterized by different power delay profiles. Federated learning is capable of addressing this limited observability, but often struggles with data heterogeneity. To tackle this challenge, we propose a neural collapse (NC) inspired deep supervised federated learning (NCDSFL) algorithm. |
| title | Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2506.19476 |