| Project Title: | Federated Learning for Privacy-preserving Video Caching Network |
| Funding Agency: | National Science Foundation (USA), Institute of Information and Communication Technology Planning and Evaluation (Korea) |
| Award Number: | 2152646 (USC) |
Caching at the wireless edge is a promising method for improving the spectral efficiency and quality of
video streaming. Caching strategies usually require knowledge of the video demands, as well as the
historical preferences of the individual users. This information needs to be combined with information
about the wireless network structure and state to develop deterministic or stochastic caching policies.
Yet, in many cases it is undesirable to share detailed user proles with the cache and/or wireless
network operator. The main motivation for avoiding such sharing is user privacy; furthermore video
streaming providers do not want to share user popularity since this is important condential business
information. A promising way out of this dilemma is federated learning (FL), which allows localized
training and exchange of machine learning models. The current project thus investigates the advanced federated learning technique for an intelligent and
privacy-preserving video caching and computing system and to novel federated learning techniques that
preserve user privacy while retaining eciency of caching.
Specifically, the goals are to: