Learning-based Multi-Objective Resource Allocation for Over-the-Air Federated Learning

Tu, Xuezhen; Zhu, Kun

doi:10.1109/globecom48099.2022.10000875

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Tu et al [27] Minimize the AMSE of the aggregation and maximize the long-term energy efficiency of the system via DRL-based framework.…”

Section: Multi-objectivementioning

confidence: 99%

“…In [26], the authors optimize a multi-objective problem that contains bandwidth and computing resource allocation to obtain a trade-off between the training time and energy consumption. In [27], the authors propose a deep reinforcement learning-based framework to minimize the AMSE of the over-the-air aggregation for different communication rounds and maximize the long-term energy efficiency of the system. In [28], the authors propose Fed-MOODS, a Multi-Objective Optimization-based Device Selection approach that significantly improves the model's convergence and performance.…”

Section: Multi-objectivementioning

confidence: 99%

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A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning

Xu,

Liu,

et al. 2023

Future Internet

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As a new distributed machine learning (ML) approach, federated learning (FL) shows great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogeneity in terms of data distribution and hardware configurations make it hard to select participants from the thousands of nodes. In this paper, we propose a multi-objective node selection approach to improve time-to-accuracy performance while resisting malicious nodes. We firstly design a deep reinforcement learning-assisted FL framework. Then, the problem of multi-objective node selection under this framework is formulated as a Markov decision process (MDP), which aims to reduce the training time and improve model accuracy simultaneously. Finally, a Deep Q-Network (DQN)-based algorithm is proposed to efficiently solve the optimal set of participants for each iteration. Simulation results show that the proposed method not only significantly improves the accuracy and training speed of FL, but also has stronger robustness to resist malicious nodes.

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“…Tu et al [27] Minimize the AMSE of the aggregation and maximize the long-term energy efficiency of the system via DRL-based framework.…”

Section: Multi-objectivementioning

confidence: 99%

Section: Multi-objectivementioning

confidence: 99%

A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning

Xu,

Liu,

et al. 2023

Future Internet

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Networking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Chen,

Yi,

Okegbile

et al. 2024

IEEE Commun. Surv. Tutorials

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Decomposition and Meta-DRL Based Multi-Objective Optimization for Asynchronous Federated Learning in 6G-Satellite Systems

Zhou,

Lei,

Zhao

et al. 2024

IEEE J. Select. Areas Commun.

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Learning-based Multi-Objective Resource Allocation for Over-the-Air Federated Learning

Cited by 5 publications

References 10 publications

A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning

A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning

Networking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Decomposition and Meta-DRL Based Multi-Objective Optimization for Asynchronous Federated Learning in 6G-Satellite Systems

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