FLaaS6G: Federated Learning as a Service in 6G Using Distributed Data Management Architecture

AI/ML is envisioned to play an essential role in 6G mobile communication systems. The privacy-preserving capabil-ities of Federated Learning (FL) make it promising in vertical applications; however, the central server-based system and lack of trusted data management limit its widespread use. To effectively support FL as a service from a network architecture perspective, this work provides a comprehensive design including three key features: First, the network architecture enables transparent and traceable data management based on Distributed Ledger Technology (DLT) platform, and realizes distributed and off-chain data storage by adopting Distributed Data Storage Entity (DDSE). Second, the central aggregator of an FL service is decoupled from the data management scheme mentioned above, and is decentralized through smart contracts for aggregator selection among a set of aggregator candidates, with the selected aggregator subsequently responsible for client selection and model aggregation. Third, a completed set of procedures for FL services operations is defined. A simulation system is developed to verify the feasibility of the proposed architecture and to study the impact of introducing the data management mechanisms on the overall performance overhead. The results show that the impact is related to the FL settings, with a worst-case time overhead of 15% observed in selected test cases, i.e., 15% of the total time spent on the interactions with the DLT platform and DDSE.