Lecture: Federated Learning - Advances and Open Challenges

Lecture: Federated Learning - Advances and Open Challenges

Administrative information

Title

 Federated Learning - Advances and Open Challenges
Duration 45 - 60
Module C
Lesson Type Lecture
Focus Technical - Future AI
Topic Advances in ML models through an HC lens - A result Oriented Study

 

Keywords

Federated Learning,Decentralised data,Scalability,Non-convex optimization,Bias and Fairness,

 

Learning Goals

  • Identify and discuss the advances of Federated Learning
  • Recognise Open challenges of federated learning and discuss the proposed solutions

 

Expected Preparation

Lesson materials

 

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

 

Instructions for Teachers

The goal of this lecture is to teach students how machine learning models can be refined when the model has been deployed on a device. This lecture should cover some of the basic concepts in FL but focus on the open problems, advances, and challenges outlined below.

 

Outline

Duration Description Concepts Activity Material
5 min Federated learning (FL) lifecycle & training Lifecycle (problem identification, instrumentation, prototyping, training, evaluation, deployment), Training (selection, broadcast, computation, aggregation, model update) Taught session and examples Lecture materials
10 min Algorithmic & practical challenges Fully Decentralized / Peer-to-Peer Distributed Learning, SGD and network topologies, compression and quantization methods, Blockchain implementation of central server for aggregation, Cross-Silo (FL), Split learning Taught session and examples Lecture materials
5 min Efficiency & Effectiveness Indepentant & identically distributed data (IID Data), Strategies for Dealing with Non-IID Data, Optimization Algorithms for FL Taught session and examples Lecture materials
10 min Model security (privacy & model attack) Actors, Threat Models, Privacy in Depth, Secure Computations, Trusted execution environments, Local/Distributed/Hybrid differential privacy, Verifiability, External Malicious Actors Taught session and examples Lecture materials
5 min Fairness & Bias Bias in Training Data, Fairness Without Access to Sensitive Attributes, Improving model diversity, Taught session and examples Lecture materials
5 min Systematic challenges Development and deployment challenges, code deployment, monitoring and debugging, System induced bias, Parameter tunning Taught session and examples Lecture materials
5 min Conclusion, questions and answers Summary Conclusions Lecture materials

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 22:18:50
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Acknowledgement

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

HCAIM Consortium. (z.d.).

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https://maken.wikiwijs.nl/202193/Interactive_session__Filter_Bubble___Political__Corporate_and_Geographical

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