Validating a System Development Kit for edge federated learning
|Coordinator||Scaleout Systems AB|
|Funding from Vinnova||SEK 4 918 474|
|Project duration||October 2023 - September 2025|
|Venture||Advanced and innovative digitalisation|
|Call||Advanced and innovative digitalization 2023 - call two|
Purpose and goal
The project´s primary aim is to increase our understanding of scalability and cyber security in federated machine learning specifically for cloud edge applications. We will also further develop and validate a system development kit for federated machine learning, FEDn, for large-scale applications in fleet intelligence.
Expected results and effects
Concrete objectives include: - A new testbed for large-scale experiments with millions of clients in a federation. - An increased understanding of the effect of so-called "stragglers" in federated machine learning with large numbers of clients. - New theory and analysis around how selection and partitioning of clients enters a formal security analysis. - New aggregation strategies that improve both scalability and security.
Planned approach and implementation
The project is organized into four work packages that will largely overlap in time. We will form a cross-organizational project group with members from Scaleout, Uppsala Universitet and another partner. We plan to meet regularly via Zoom (every two weeks) and in physical meetings once a quarter.