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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 |
Federated Learning,Decentralised data,Scalability,Non-convex optimization,Bias and Fairness,
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The materials of this learning event are available under CC BY-NC-SA 4.0.
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.
| Duration | Description | Concepts | Activity | Material |
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| 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 |
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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