Administrative information
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Title
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Theory of Federated Learning (Profiling and Personalization) |
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| 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 |
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Keywords
Federated Learning, knowledge-based system, privacy preservation,
Learning Goals
- Provide the motivations for doing Federated Learning
- Provide an initial understanding of the basic techniques for Federated Learning
- Discuss the main limitations and the challenges connected to them
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- Basic knowledge of probability and statistics
- Basic understanding of deep learning training (SGD, backpropagation algorithm) and evaluation techniques
- Introduction to machine learning and deep learning concepts given in previous lectures
Optional for Students
None.
References and background for students
None.
Recommended for Teachers
- P. Kairouz, et al. "Advances and Open Problems in Federated Learning", Foundations and Trends® in Machine Learning: Vol. 14: No. 1–2, pp 1-210. - URL
- Konečný, Jakub and McMahan, H. Brendan and Ramage, Daniel and Richtárik, Peter. "Federated Optimization: Distributed Machine Learning for On-Device Intelligence". arXiv 2016.
- Chen Zhang, et al., A survey on federated learning, Knowledge-Based Systems, Volume 216, 2021, 106775, ISSN 0950-7051 - URL
- Google Federated Learning and AI
- Federated Learning: Collaborative Machine Learning without Centralized Training Data
- Deep learning -> Federated learning in 10 lines
- tensorflow/federated
Lesson materials
The materials of this learning event are available under CC BY-NC-SA 4.0.
Instructions for Teachers
- Provide an overview of the techniques, their pros and cons
- Propose pop-up quizzes
The learning event shall refer to model types, their evaluation and possible optimization techniques.
Outline
| 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 |
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
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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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