Abstract
Along with continuous evolution, the future 6G network will become a converged “Cloud-Edge-Terminal” ecosystem which can carry various crucial AI applications on edge computing units, formulating an ubiquitous “Edge Intelligence” paradigm to enable differentiated service innovations and empower intelligent transformation of vertical industries. However, due to issues of data security, user privacy, wireless network transmission capability and etc., it is not feasible for conventional machine learning methods to build AI models by directly collecting massive distributed edge data together, and hence resulting a large number of “isolated data islands” in the edge units. In order to break the data sharing barrier and drive cross-edge data cooperation, this paper studies a federated learning based AI model training method by which sensitive raw data can be maintained and protected in its original edge units. Based on the general scheme, some challenging problems are discussed to implement this new paradigm in practical scenarios, and the corresponding promising solutions and key techniques are proposed to inspire further researches.
This work is supported by National Key R&D Program of China (2018YFB1402701).
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Wang, H., Hu, J., Xing, C., Zhang, LJ. (2022). Federated Learning for 6G Edge Intelligence: Concepts, Challenges and Solutions. In: Pan, Y., Mao, ZH., Luo, L., Zeng, J., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2021. AIMS 2021. Lecture Notes in Computer Science(), vol 12987. Springer, Cham. https://doi.org/10.1007/978-3-030-96033-9_9
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