WASP project - Deployment and automated tuning of ML functions in distributed RAN environments
Title: WASP project - Deployment and automated tuning of ML functions in distributed RAN environments
SNIC Project: Berzelius-2022-10
Project Type: LiU Berzelius
Principal Investigator: Martin Isaksson <martin.isaksson@gmail.com>
Affiliation: Kungliga Tekniska högskolan
Duration: 2022-01-20 – 2022-08-01
Classification: 10201
Homepage: https://wasp-sweden.org/
Keywords:

Abstract

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.