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https://doi.org/10.1109/tvt.2020.2984369
Copy DOIPublication Date: Jun 1, 2020 | |
Citations: 80 |
We propose a novel communication efficient and privacy preserving federated learning framework for enhancing the performance of Internet of Vehicles (IoV), wherein on-vehicle learning models are trained by exchanging inputs, outputs and their learning parameters locally. Moreover, we use analytic modeling as a tool for reasoning and developing the required IoV scenario and stabilize their data flow dynamics by considering TCP CUBIC streams over WiFi networks to prove our idea.
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