☆ 3.8 Proceedings Paper

Optimal Design of Hybrid Federated and Centralized Learning in the Mobile Edge Computing Systems

2021 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS) (2021)

A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

Mingzhe Chen et al.

Summary: This article discusses the challenges of training federated learning algorithms over a realistic wireless network and proposes an optimization model to minimize the FL loss function, providing a method to improve identification accuracy.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2021)

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Proceedings Paper Engineering, Electrical & Electronic

Convergence Time Minimization of Federated Learning over Wireless Networks

Mingzhe Chen et al.

ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) (2020)

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Proceedings Paper Engineering, Electrical & Electronic

Joint Optimization of Data Sampling and User Selection for Federated Learning in the Mobile Edge Computing Systems

Chenyuan Feng et al.

2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS) (2020)

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Article Engineering, Electrical & Electronic

Toward Interconnected Virtual Reality: Opportunities, Challenges, and Enablers

Ejder Bastug et al.

IEEE COMMUNICATIONS MAGAZINE (2017)

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Optimal Design of Hybrid Federated and Centralized Learning in the Mobile Edge Computing Systems

相关参考文献

A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

Convergence Time Minimization of Federated Learning over Wireless Networks

Joint Optimization of Data Sampling and User Selection for Federated Learning in the Mobile Edge Computing Systems

Toward Interconnected Virtual Reality: Opportunities, Challenges, and Enablers

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