On Signal Peak Power Constraint of Over-the-Air Federated Learning
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908934066929664 |
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| author | Bielefeld, Lorenz Zheng, Paul Hanay, Oner Zhu, Yao Hu, Yulin Schmeink, Anke |
| author_facet | Bielefeld, Lorenz Zheng, Paul Hanay, Oner Zhu, Yao Hu, Yulin Schmeink, Anke |
| contents | Federated learning (FL) has been considered a promising privacy preserving distributed edge learning framework. Over-the-air computation (AirComp) leveraging analog transmission enables the aggregation of local updates directly over-the-air by exploiting the superposition properties of wireless multiple-access channels, thereby alleviating the communication bottleneck issues of FL compared with digital transmission schemes. This work points out that existing AirComp-FL overlooks a key practical constraint, the instantaneous peak-power constraints due to the non-linearity of radio-frequency power amplifiers. Operating directly in non-linear region causes in-band and out-of-band distortions. We present and analyze the effect of the default method that limits the signal's peak power and out-of-band distortions, iterative amplitude clipping combined with filtering. We investigate the effect of imposing instantaneous peak-power constraints in AirComp-FL for both single-carrier and multi-carrier orthogonal frequency-division multiplexing (OFDM) systems. Simulation results demonstrate that, in practical settings, the instantaneous transmit power in AirComp-FL regularly exceeds the power-amplifier linearity limit. As the first work of this line of research, it is essential to evaluate if this is an actual problem that has an impact on FL performance. We therefore apply the classic method of iterative clipping and filtering, and show that the FL performance degrades more or less depending on the scenarios. The degradation becomes pronounced especially in multi-carrier OFDM systems due to the in-band distortions caused by clipping and filtering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Signal Peak Power Constraint of Over-the-Air Federated Learning Bielefeld, Lorenz Zheng, Paul Hanay, Oner Zhu, Yao Hu, Yulin Schmeink, Anke Signal Processing Federated learning (FL) has been considered a promising privacy preserving distributed edge learning framework. Over-the-air computation (AirComp) leveraging analog transmission enables the aggregation of local updates directly over-the-air by exploiting the superposition properties of wireless multiple-access channels, thereby alleviating the communication bottleneck issues of FL compared with digital transmission schemes. This work points out that existing AirComp-FL overlooks a key practical constraint, the instantaneous peak-power constraints due to the non-linearity of radio-frequency power amplifiers. Operating directly in non-linear region causes in-band and out-of-band distortions. We present and analyze the effect of the default method that limits the signal's peak power and out-of-band distortions, iterative amplitude clipping combined with filtering. We investigate the effect of imposing instantaneous peak-power constraints in AirComp-FL for both single-carrier and multi-carrier orthogonal frequency-division multiplexing (OFDM) systems. Simulation results demonstrate that, in practical settings, the instantaneous transmit power in AirComp-FL regularly exceeds the power-amplifier linearity limit. As the first work of this line of research, it is essential to evaluate if this is an actual problem that has an impact on FL performance. We therefore apply the classic method of iterative clipping and filtering, and show that the FL performance degrades more or less depending on the scenarios. The degradation becomes pronounced especially in multi-carrier OFDM systems due to the in-band distortions caused by clipping and filtering. |
| title | On Signal Peak Power Constraint of Over-the-Air Federated Learning |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2512.23381 |