IEEE SPS Seasonal School on Networked Federated Learning: Theory, Methods and Applications

Many important application domains generate distributed collections of local datasets. Networked federated learning allows to train tailored models for each local dataset in a col- laborative fashion.

This seasonal school teaches some of the theoretical and al- gorithmic underpinnings of federated learning. We illustrate key concepts using the toy example of a personalized Covid-19 diagnosis smartphone app.

This seasonal school is organised as three modules:

• Basics of Machine Learning
• Networked Data
• Networked Models

Each module consists of lectures and coding assignments with Python notebooks.

This seasonal school is inspired by the recent Live-Project by Alexander Jung, Assistant Professor for machine learning at the Department of Computer Science, Aalto University.

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