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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 ...
Oct 14, 2024 · The research demonstrates how the proposed framework can effectively utilize wearable devices to detect real-time stress, offering insights into.
Jul 30, 2024 · This work is pioneering in addressing the problem of aggregating heterogeneous models within hierarchical FL systems spanning IoT, edge, and cloud environments.
HierFedPDP is a personalized local differential privacy mechanism that tailors privacy settings based on data sensitivity, thereby enhancing data protection.
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[PDF] Enhancing Privacy via Hierarchical Federated Learning
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This paper introduces a DP based privacy preserving method with hierarchical over-the-air FL and addresses both communication and privacy aspects in an end-to- ...
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To optimize the processing power of edge and cloud servers, a hierarchical federated learning (HFL) system involving clients, edge servers, ...
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Aug 12, 2024 · This work proposes a flexible framework for drowsiness identification by using HFL, FL, and ML over EEG and EOG data.
In this review, we examine various decentralized learning methodologies, including federated learning, split learning, swarm learning, gossip learning, edge ...
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