Federated Learning for 6G: A Survey From Perspective of Integrated Sensing, Communication and Computation

doi:10.12142/ZTECOM.202302005

Abstract

Abstract:

With the rapid advancements in edge computing and artificial intelligence, federated learning (FL) has gained momentum as a promising approach to collaborative data utilization across organizations and devices, while ensuring data privacy and information security. In order to further harness the energy efficiency of wireless networks, an integrated sensing, communication and computation (ISCC) framework has been proposed, which is anticipated to be a key enabler in the era of 6G networks. Although the advantages of pushing intelligence to edge devices are multi-fold, some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC. This paper provides a comprehensive survey of FL, with special emphasis on the design and optimization of ISCC. We commence by introducing the background and fundamentals of FL and the ISCC framework. Subsequently, the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed. Finally, design guidelines are provided for the incorporation of FL and ISCC. Overall, this paper aims to contribute to the understanding of FL in the context of wireless networks, with a focus on the ISCC framework, and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.

Key words: integrated sensing, communication and computation, federated learning, data heterogeneity, limited resources

ZHAO Moke, HUANG Yansong, LI Xuan. Federated Learning for 6G: A Survey From Perspective of Integrated Sensing, Communication and Computation[J]. ZTE Communications, 2023, 21(2): 25-33.

Figures/Tables 6

References 66

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Challenge	Specific Method	Advantages and Disadvantages
Participant selection	Participating clients are selected based on the heterogeneous nature of the data, quality of participants and training, and resource constraints.	Selecting participants can make full use of resources and is conducive to continuous training. However, when the data scale is too large, the overall performance cannot be guaranteed in the scenario of edge intelligence applications, and the training process needs to be optimized.
Adaptive aggregation	The best tradeoff is found between local updates and global parameter aggregation under a given resource budget to speed up the local training process.	By adapting the frequency of global aggregation, the performance of the model can be improved, and the utilization of available resources can be improved. However, the convergence of adaptive aggregation schemes currently only considers convex loss functions.
Incentive mechanism	FL requires an effective incentive mechanism for participation and balances rewards and limited communication and computing resources to improve data quality.	By quantifying data quality, the overall benefit of FL is generally improved, but due to the heterogeneity of the environment, the excitation obtained by different edge devices in FL does not match, making it difficult to balance game rewards and resource consumption.
Model compression	The transmission model is compressed to improve the communication efficiency between the server and client. Knowledge distillation exchanges model outputs, allowing edge devices to adopt larger local models.	Client-to-server parameter compression may cause convergence problems, increase computational complexity, and reduce training accuracy. Knowledge distillation alleviates the problem of independent and identical distribution of data to a certain extent, but the quality of wireless channel will affect the accuracy of model training.
Privacy protection	Privacy protection may be achieved through the inference of attacks, the encryption of data and models, and the improvement of privacy protection performance by blockchain technology.	FL may solve the privacy leakage problems caused by the model parameter sharing and multi-party communication and cooperation mechanism of FL. However, further research is needed when it comes to the security problems caused by data poisoning and the removal of traces left by participants’ data in the local model, etc.

Challenge	Specific Method	Advantages and Disadvantages
Participant selection	Participating clients are selected based on the heterogeneous nature of the data, quality of participants and training, and resource constraints.	Selecting participants can make full use of resources and is conducive to continuous training. However, when the data scale is too large, the overall performance cannot be guaranteed in the scenario of edge intelligence applications, and the training process needs to be optimized.
Adaptive aggregation	The best tradeoff is found between local updates and global parameter aggregation under a given resource budget to speed up the local training process.	By adapting the frequency of global aggregation, the performance of the model can be improved, and the utilization of available resources can be improved. However, the convergence of adaptive aggregation schemes currently only considers convex loss functions.
Incentive mechanism	FL requires an effective incentive mechanism for participation and balances rewards and limited communication and computing resources to improve data quality.	By quantifying data quality, the overall benefit of FL is generally improved, but due to the heterogeneity of the environment, the excitation obtained by different edge devices in FL does not match, making it difficult to balance game rewards and resource consumption.
Model compression	The transmission model is compressed to improve the communication efficiency between the server and client. Knowledge distillation exchanges model outputs, allowing edge devices to adopt larger local models.	Client-to-server parameter compression may cause convergence problems, increase computational complexity, and reduce training accuracy. Knowledge distillation alleviates the problem of independent and identical distribution of data to a certain extent, but the quality of wireless channel will affect the accuracy of model training.
Privacy protection	Privacy protection may be achieved through the inference of attacks, the encryption of data and models, and the improvement of privacy protection performance by blockchain technology.	FL may solve the privacy leakage problems caused by the model parameter sharing and multi-party communication and cooperation mechanism of FL. However, further research is needed when it comes to the security problems caused by data poisoning and the removal of traces left by participants’ data in the local model, etc.

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