Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control

Zhang, Zixuan; Jiang, Yuning; Shi, Yuanming; Chen, Wei

doi:10.1109/gcwkshps56602.2022.10008598

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…The central server plays a crucial role in coordinating the exchange of knowledge between agents by broadcasting, receiving action-value estimates and selecting actions with the highest UCB (Upper Confidence Bound) value. There are still many ongoing explorations in other areas where FedRL is being applied, including energy management [133,134], electric vehicle charging and uncharging [135].…”

Section: Federated Reinforcement Learningmentioning

confidence: 99%

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Ji,

Tan,

Saravirta

et al. 2023

Preprint

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Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks.We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed federated X learning, where X includes multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. This survey reviews the state of the art, challenges, and future directions.

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Section: Federated Reinforcement Learningmentioning

confidence: 99%

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Ji,

Tan,

Saravirta

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Emerging trends in federated learning: from model fusion to federated X learning

Ji,

Tan,

Saravirta

et al. 2024

Int. J. Mach. Learn. & Cyber.

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Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed federated X learning, where X includes multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. In addition to reviewing state-of-the-art studies, this paper also identifies key challenges and applications in this field, while also highlighting promising future directions.

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Reinforcement learning for electric vehicle charging scheduling: A systematic review

Zhao,

Lee,

Yan

et al. 2024

Transportation Research Part E: Logistics and Transportation Re

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Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control

Cited by 5 publications

References 28 publications

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Emerging trends in federated learning: from model fusion to federated X learning

Reinforcement learning for electric vehicle charging scheduling: A systematic review

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