TRUSTroke webinar on Federated Learning

Europe/Zurich
4/3-001 (CERN)

4/3-001

CERN

18
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Description

Federated Learning is a machine learning approach that allows a model to be trained across multiple decentralised devices or servers holding local data samples, without exchanging them. Instead of sending data to a central server, updates to the model are computed locally on each device, and only model parameters are aggregated or combined. This approach minimises the risk of exposing sensitive user data.  It strikes a balance between model performance and data privacy, making it a valuable approach in applications such as healthcare where data privacy is a top priority.

Please use the link below to join the webinar:
Passcode: 033428

https://cern.zoom.us/j/65647682273?pwd=ZFFtNEV3Y1dGSE95L2d4YnIySkkvQT09

The TRUSTroke project is funded by the European Union in the call HORIZON-HLTH-2022-STAYHLTH-01-two-stage under grant agreement No-101080564

    • 12:00 PM 1:00 PM
      TRUSTroke webinar on Federated Learning 1h

      FL theories and applications (Stefano Savazzi)
      Network, Security and privacy (Michele Carminati and Alessandro Redondi)
      CERN FL operating platform (CAFEIN) demo (Diogo Reis Santos)

      Speakers: Alessandro Redondi (Politecnico di Milano), Diogo Reis Santos (CERN), Luigi Serio (CERN), Michele Carminati (Politecnico di Milano), Stefano Savazzi (CNR)