Practical: Federated Learning - Train deep models

Practical: Federated Learning - Train deep models

Administrative information

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  

 

Keywords

Federated Learning,Tensorflow,

 

Learning Goals

  • Understand how to train models using the Federated Learning framework
  • Understand how local data distribution affects the federated learning
  • Becoming familiar with a high-level framework like TensorFlow

 

Expected Preparation

Obligatory for Students

  • Students should have a basic understanding of deep learning concepts and techniques
  • Basic understanding of deep learning training (SGD, backpropagation algorithm) and evaluation techniques

Optional for Students

  • A bit of knowledge of the TensorFlow framework and Python programming language

Lesson materials


The materials of this learning event are available under CC BY-NC-SA 4.0.

 

Instructions for Teachers

  • Provide a hands-on lecture where students can learn from guided exercises
  • Propose pop-up quizzes

Outline

Duration Description Concepts Activity
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

 

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

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

 

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities

HCAIM Consortium

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Auteur
HCAIM Consortium
Laatst gewijzigd
2024-02-14 22:44:04
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Gebruikte Wikiwijs Arrangementen

HCAIM Consortium. (z.d.).

Acknowledgement

https://maken.wikiwijs.nl/198386/Acknowledgement

HCAIM Consortium. (z.d.).

Lecture: Trust, Normativity and Model Drift

https://maken.wikiwijs.nl/202206/Lecture__Trust__Normativity_and_Model_Drift

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