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

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 are 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.

Keywords: integrated sensing, communication and computation; federated learning; data heterogeneity; limited resources