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Oct 20, 2023 · We present a novel hierarchical FL framework called EPHFL. It leverages the Diffie-Hellman algorithm and pseudorandom technology to enhance the privacy of FL.
Its hierarchical architecture can effectively schedule devices in the IoV to accomplish FL and reduce the communication overhead of each device, dramatically ...
Title: An Enhanced Privacy-Preserving Hierarchical Federated Learning Framework for IoV. Authors: Jiacheng Luo, Xuhao Li, Hao Wang 0189, Dongwan Lan ...
A Hierarchical Asynchronous Federated Learning Privacy ...
www.researchgate.net › publication › 37...
To address these issues, this paper proposes a hierarchical federated learning framework for IoVs environment that combines synchronous and asynchronous methods ...
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2 days ago · By employing federated learning, IoV-SFL framework ensures that data remains on local devices, preserving user privacy, while consortium ...
In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed ...
A Hierarchical Asynchronous Federated Learning Privacy ...
www.springerprofessional.de › a-hierarc...
To address these issues, this paper proposes a hierarchical federated learning framework for IoVs environment that combines synchronous and asynchronous methods ...
Sep 8, 2023 · A novel vehicle-clustering paradigm is designed to group passing vehicles based on the Bloom-filter-based compact representation of data complementarity.
FL is introduced into autonomous driving to preserve vehicular privacy by keeping original data in a local vehicle and sharing the training model parameter ...
Federated learning (FL) as a novel paradigm in Artificial Intelligence (AI), ensures enhanced privacy by eliminating data centralization and brings learning ...