WASP project - Deployment and automated tuning of ML functions in distributed RAN environments
||WASP project - Deployment and automated tuning of ML functions in distributed RAN environments|
||Martin Isaksson <firstname.lastname@example.org>|
||Kungliga Tekniska högskolan|
||2022-01-20 – 2022-08-01|
This project will do research on distributed and decentralized machine learning methods, for example Federated Learning (FL), gossip learning, transfer learning and reinforcement learning - specifically the aspects of such functions that apply to a distributed cloud/virtual 5G RAN environment.
FL is a promising framework for distributed learning when data is private
and sensitive. However, the state-of-the-art solutions in this framework are
not optimal when data is heterogeneous and non-IID. FL, especially device-based FL is also resource intensive due to the very large amount of clients needed to investigate these methods.