Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning

Zhu, Tianqing; Zhou, Wei; Ye, Dayong; Cheng, Zishuo; Li, Jin

doi:10.1109/jiot.2021.3086910

“…The logistic growth model is also known as the retarded growth model. 44 The model is typically used in fields such as describing populations, the growth of infectious diseases, and the forecast of commodity sales. The specific algorithmic idea of the model is as follows: the first enter the crawled epidemic data, automatically calculate the number of days corresponding to the input data, use the abscissa to indicate the number of days, the ordinate to indicate the number of cases, and use the input data as scattered points The diagram prints out.…”

Section: Proposed Modelmentioning

confidence: 99%

An intelligent forecast for COVID‐19 based on single and multiple features

Wang

¹

,

Zhang

²

,

Zhang

³

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

It is urgent to identify the development of the Corona Virus Disease 2019 (COVID-19) in countries around the world. Therefore, visualization is particularly important for monitoring the COVID-19. In this paper, we visually analyze the real-time data of COVID-19, to monitor the trend of COVID-19 in the form of charts. At present, the COVID-19 is still spreading. However, in the existing works, the visualization of COVID-19 data has not established a certain connection between the forecast of the epidemic data and the forecast of the epidemic. To better predict the development trend of the COVID-19, we establish a logistic growth model to predict the development of the epidemic by using the same data source in the visualization. However, the logistic growth model only has a single feature. To predict the epidemic situation in an allround way, we also predict the development trend of the COVID-19 based on the Susceptible Exposed Infected Removed epidemic model with multiple features. We fit the data predicted by the model to the real COVID-19 epidemic data. The simulation results show that the predicted epidemic development trend is consistent with the actual epidemic development trend, and our model performs well in predicting the trend of COVID-19.

show abstract

“…Additionally, to deal with the different types of optimization tasks, variants of FRL are being studied. Zhu et al present a resource allocation method for edge computing system, called concurrent federated reinforcement learning (CFRL) [95]. The edge node continuously receives tasks from serviced IoT devices and stores those tasks in a queue.…”

Section: A Frl For Edge Computingmentioning

confidence: 99%

Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Qi¹,

Zhou²,

Lei³

et al. 2021

Preprint

View full text Add to dashboard Cite

This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of Federated Learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, i.e., Horizontal Federated Reinforcement Learning (HFRL) and Vertical Federated Reinforcement Learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL.

show abstract

“…Additionally, to deal with the different types of optimization tasks, variants of FRL are being studied. Zhu et al present a resource allocation method for edge computing systems, called concurrent federated reinforcement learning (CFRL) [95] . The edge node continuously receives tasks from serviced IoT devices and stores those tasks in a queue.…”

Section: Frl For Edge Computingmentioning

confidence: 99%

Federated reinforcement learning: techniques, applications, and open challenges

Qi¹,

Zhou²,

Lei³

et al. 2021

View full text Add to dashboard Cite

This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of Federated Learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, i.e., Horizontal Federated Reinforcement Learning (HFRL) and Vertical Federated Reinforcement Learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL.

show abstract

Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning

Cited by 55 publications

References 37 publications

An intelligent forecast for COVID‐19 based on single and multiple features

An intelligent forecast for COVID‐19 based on single and multiple features

Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Federated reinforcement learning: techniques, applications, and open challenges

Contact Info

Product

Resources

About