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Sixth-generation (6G) networks anticipate intelligently supporting a widerange of smart services and innovative applications. Such a context urges aheavy usage of Machine Learning (ML) techniques, particularly Deep Learning(DL), to foster innovation and ease the deployment of intelligent networkfunctions/operations, which are able to fulfill the various requirements of theenvisioned 6G services. Specifically, collaborative ML/DL consists of deployinga set of distributed agents that collaboratively train learning models withoutsharing their data, thus improving data privacy and reducing thetime/communication overhead. This work provides a comprehensive study on howcollaborative learning can be effectively deployed over 6G wireless networks.In particular, our study focuses on Split Federated Learning (SFL), a techniquerecently emerged promising better performance compared with existingcollaborative learning approaches. We first provide an overview of threeemerging collaborative learning paradigms, including federated learning, splitlearning, and split federated learning, as well as of 6G networks along withtheir main vision and timeline of key developments. We then highlight the needfor split federated learning towards the upcoming 6G networks in every aspect,including 6G technologies (e.g., intelligent physical layer, intelligent edgecomputing, zero-touch network management, intelligent resource management) and6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomoussystems). Furthermore, we review existing datasets along with frameworks thatcan help in implementing SFL for 6G networks. We finally identify key technicalchallenges, open issues, and future research directions related to SFL-enabled6G networks.

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions