Title |
Federated Learning - Train deep models | ![]() |
Duration | 150 min | |
Module | C | |
Lesson Type | Practical | |
Focus | Technical - Future AI | |
Topic | Advances in ML models through a HC lens - A result Oriented Study |
Federated Learning,Tensorflow,
The materials of this learning event are available under CC BY-NC-SA 4.0.
Duration | Description | Concepts | Activity |
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20 min | Introduction to the framework: how to code a simple federated learning system | Tools introduction | Introduction to main tools |
60 min | Federated Training: the easy way. How to apply train models with federated learning based on iid local data | Federated Average | Practical session and working examples |
60 min | Federated training: the hard way. How does heterogeneity affect Federated Average and what can we do | Challenges connected to Federated Learning | Practical session and working examples |
10 min | Conclusion, questions and answers | Summary | Conclusions |
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
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The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068. The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities |