SciLifeLab Workshop on Federated Machine Learning
June 23 @ 13:00 – 16:00 CEST
Location: Online via Zoom
Program:
13.00 – 13.45 Presentation: Introduction to Federated Machine Learning
14.00 – 16.00 Hands-on workshop: How to set up, run and deploy a federated learning project using the FEDn open source solution
Note that it is possible to attend the first presentation only, or both presentation and workshop.
What is Federated Machine Learning?
Federated learning enables several organizations / groups to collaborate on machine learning models without needing to directly share sensitive or confidential data with each other.It is a distributed machine learning approach which enables training on decentralised data. A server coordinates a network of nodes, each of which has local, private training data. These nodes contribute to the construction of a global model by training on local data, and the server combines non-sensitive node model contributions into the global model.
For a short (10min ) introduction see: https://www.youtube.com/watch?v=jbLHRtGWPL8
Who should attend?
1.Researchers who are interested in learning more about FedML
2.Researchers who are interested in testing FedML hands-on in the FEDn solution
About the organizers
Scaleout consists of a team of data scientists, machine learning engineers, software engineers, and entrepreneurs with experience from both industry and academic research in AI and applied machine learning, cloud and fog computing, and scientific computing from Uppsala University. We’re working on a platform for end-to-end privacy-preserving machine learning with a focus on helping organisations put advanced machine learning and DevOps technologies into production.
Registration
The presentation and the workshop is free of charge but requires registration. The number of seats for the hands-on part of the workshop is limited to 20 people so only register for the workshop if you are interested in actively participating in the tutorial.
Contact: Prof. Ola Spjuth, AI coordinator, SciLifeLab Data Centre.
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