Enabling Resource-Efficient AIoT System With Cross-Level Optimization: A Survey

Liu, Sicong; Guo, Bin; Fang, Cheng; Wang, Ziqi; Luo, Shiyan; Zhou, Zimu; Yu, Zhiwen

doi:10.1109/comst.2023.3319952

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2024

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Cited by 11 publications

References 267 publications

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DeepSwarm: towards swarm deep learning with bi-directional optimization of data acquisition and processing

Liu,

Guo,

Wang

et al. 2024

Front. Comput. Sci.

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DeepSwarm: towards swarm deep learning with bi-directional optimization of data acquisition and processing

Liu,

Guo,

Wang

et al. 2024

Front. Comput. Sci.

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Combining federated learning and control: A survey

Weber,

Gurtner,

Lobe

et al. 2024

IET Control Theory & Appl

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This survey provides an overview of combining federated learning (FL) and control to enhance adaptability, scalability, generalization, and privacy in (nonlinear) control applications. Traditional control methods rely on controller design models, but real‐world scenarios often require online model retuning or learning. FL offers a distributed approach to model training, enabling collaborative learning across distributed devices while preserving data privacy. By keeping data localized, FL mitigates concerns regarding privacy and security while reducing network bandwidth requirements for communication. This survey summarizes the state‐of‐the‐art concepts and ideas of combining FL and control. The methodical benefits are further discussed, culminating in a detailed overview of expected applications, from dynamical system modelling over controller design, focusing on adaptive control, to knowledge transfer in multi‐agent decision‐making systems.

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