In this paper, we propose a channel perturbation method so that no edge device is excluded due to experiencing deep fade. To this end, OAC is performed in ...
Abstract—Federated learning (FL) is a promising technology which trains a machine learning model on edge devices in a distributed manner orchestrated by a ...
Abstract—Federated learning (FL) is a promising technology which trains a machine learning model on edge devices in a distributed manner orchestrated by a ...
Apr 27, 2024 · Federated learning is a decentralized machine learning approach where models are trained across multiple devices or servers holding local ...
Oct 5, 2022 · By exploiting the waveform superposition property of multiple access channels, OtA FL enables the users to transmit their updates ...
Feb 9, 2024 · This paper provides a holistic review of progress in OTA-FL and points to potential future research directions.
Jun 5, 2023 · By employing federated learning techniques, satellite sys- tems can benefit from collective intelligence and improved models while optimizing ...
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However, OTA-FL is sensitive to wireless channel fading, which could significantly diminish the learning accuracy of OTA-FL. To address this challenge, in this ...
Traditional perturbation-based methods provide privacy protection while sacrificing the training accuracy due to the reduced signal-to-noise ratio. In this work ...
Jun 5, 2023 · Federated learning in satellites offers several advantages. Firstly, it ensures data privacy and security, as sensitive data remains on the ...