Federated-Learning-Enabled Intelligent Fog Radio Access Networks: Fundamental Theory, Key Techniques, and Future Trends

By Zhongyuan Zhao, Chenyuan Feng, Howard H. Yang, Xueting Luo. 2020

103 citations recorded by the source corpus.

The rise of big data and AI boosts the development of future wireless networks. However, due to the high cost of data offloading and model training, it is challenging to implement network intelligence based on the existing centralized learning strategies, especially at the edge of networks. To provide a feasible solution, a paradigm of federated learning- enabled intelligent F-RANs is proposed, which can take full advantage of fog computing and AI. The fundamental theory with respect to the accuracy loss correction and the model compression is studied, which can provide some insights into the design of federated learning in F-RANs. To support the implementation of federated learning, some key techniques are introduced to fully integrate the communication, computation, and storage capability of F-RANs. Moreover, future trends of federated learning-enabled intelligent F-RANs, such as potential applications and open issues, are discussed.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks (2019, background): “spectrum, only a portion of the UEs can be selected to access the radio channel and send their trained updates in each global aggregation [5, 11].”
Scheduling Policies for Federated Learning in Wireless Networks (2019, background): “Apart from the scheduling issue, the vanilla training approach per Algorithm 1 suffers potential setback of slow convergence [26], especially when the loss function or regularizer has complicated forms.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “[18] FL and fog radio access networks An overview on the use of FL in fog radio access networks, with a discussion of the fundamental FL theory with respect to the accuracy loss correction and the model compression.”
Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, unknown): “Each user is treated equally without bias in this process [126].”
A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks (2019, background): “First, the works in [7], [19], and [22] only provided a high-level exposition of the challenges of communication in FL.”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “Moreover, the authors of [97] discussed the implementation challenges of FL techniques for the full integration of the communication, computation, and storage capabilities of F-RANs.”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, methodology): “The design and some key use cases of FLchain implementation in edge computing are discussed in Section IV, while the applications of FLchain in edge computing are analyzed in Section V.”