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Validating a System Development Kit for edge federated learning

Reference number
Coordinator Scaleout Systems AB
Funding from Vinnova SEK 4 918 474
Project duration October 2023 - September 2025
Status Ongoing
Venture Advanced digitalization - Enabling technologies
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 effects and result

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.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 20 November 2023

Reference number 2023-01890

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