Analog Over-the-Air Federated Learning with Interference-Based Energy Harvesting
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909783674585088 |
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| author | Khel, Ahmad Massud Tota Ikhlef, Aissa Ding, Zhiguo Sun, Hongjian |
| author_facet | Khel, Ahmad Massud Tota Ikhlef, Aissa Ding, Zhiguo Sun, Hongjian |
| contents | We consider analog over-the-air federated learning, where devices harvest energy from in-band and out-band radio frequency signals, with the former also causing co-channel interference (CCI). To mitigate the aggregation error, we propose an effective denoising policy that does not require channel state information (CSI). We also propose an adaptive scheduling algorithm that dynamically adjusts the number of local training epochs based on available energy, enhancing device participation and learning performance while reducing energy consumption. Simulation results and convergence analysis confirm the robust performance of the algorithm compared to conventional methods. It is shown that the performance of the proposed denoising method is comparable to that of conventional CSI-based methods. It is observed that high-power CCI severely degrades the learning performance, which can be mitigated by increasing the number of active devices, achievable via the adaptive algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10123 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analog Over-the-Air Federated Learning with Interference-Based Energy Harvesting Khel, Ahmad Massud Tota Ikhlef, Aissa Ding, Zhiguo Sun, Hongjian Information Theory Emerging Technologies We consider analog over-the-air federated learning, where devices harvest energy from in-band and out-band radio frequency signals, with the former also causing co-channel interference (CCI). To mitigate the aggregation error, we propose an effective denoising policy that does not require channel state information (CSI). We also propose an adaptive scheduling algorithm that dynamically adjusts the number of local training epochs based on available energy, enhancing device participation and learning performance while reducing energy consumption. Simulation results and convergence analysis confirm the robust performance of the algorithm compared to conventional methods. It is shown that the performance of the proposed denoising method is comparable to that of conventional CSI-based methods. It is observed that high-power CCI severely degrades the learning performance, which can be mitigated by increasing the number of active devices, achievable via the adaptive algorithm. |
| title | Analog Over-the-Air Federated Learning with Interference-Based Energy Harvesting |
| topic | Information Theory Emerging Technologies |
| url | https://arxiv.org/abs/2509.10123 |