Hierarchical Aggregation for Federated Learning in Heterogeneous IoT Scenarios: Enhancing Privacy and Communication Efficiency

Hierarchical Aggregation for Federated Learning in Heterogeneous IoT Scenarios: Enhancing Privacy and Communication Efficiency

Federated Learning (FL) is a distributed machine-learning paradigm that enables models to be trained across multiple decentralized devices or servers holding local data without transferring the raw data to a central location. However, applying FL to heterogeneous IoT scenarios comes with several cha...

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Bibliographic Details
Main Authors: Chen Qiu, Ziang Wu, Haoda Wang, Qinglin Yang, Yu Wang, Chunhua Su
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Future Internet
Subjects:
hierarchical federated learning
lightweight
heterogeneous device
Online Access:https://www.mdpi.com/1999-5903/17/1/18
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https://www.mdpi.com/1999-5903/17/1/18

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