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Theory of Federated Learning (Profiling and Personalization) | |
| Duration | 45-60 min | |
| Module | C | |
| Lesson Type | Lecture | |
| Focus | Technical - Future AI | |
| Topic | Advances in ML models through a HC lens - A result Oriented Study |
Federated Learning, knowledge-based system, privacy preservation,
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The materials of this learning event are available under CC BY-NC-SA 4.0.
The learning event shall refer to model types, their evaluation and possible optimization techniques.
| Duration | Description | Concepts |
|---|---|---|
| 10 min | Introduction: Motivating scenario and introduction to federated learning: what it is, what it is for, when and why it is needed. | Data gravity, data privacy and the definition of enabling scenario. |
| 10 min | Federated Learning: basic concepts, system definition and algorithmic overview | Basic notions of the Federated Learning approach |
| 15 min | Federated Average algorithm: Formal definition and properties | Basic algorithm for federated learning |
| 20 min | Beyond federated average: limitations of federated average, challenges and possible solutions. | Data imbalance, personalisation, fairness |
| 5 min | Conclusion, questions and answers | Summary |
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
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