MegaData: Federated Machine Learning

28.07.2024 - 10.08.2024

Time of occurrence

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More information

Credits

3 ECTS (78 hours)

Form of study

Partially web-based

Location

Tartu

Fee

850 EUR

Teachers

Feras Mahmoud Naji Awaysheh

Study group

Information and Communication Technologies n.e.c.

Program language

English

Purpose:

1. A brief recap on Machine Learning and distributed systems.
2. Principles of Edge intelligence and the main challenges of distributed data analytics, data privacy-preserving.
3. Modern networking architectures for large-scale data analytics.
4. Open source frameworks of federated learning: limitations, challenges, and opportunities.
5. Implementations and applications of federated learning in modern systems.
6. Advanced edge operations (new trend) and Distributed ML/AI Systems: Techniques for designing efficient and accurate ML models in a distributed environment?
7. Case studies and projects: How can we implement federated learning use cases and scenarios?

Target group:

PhD and MSc

Additional information

Outcome:

On successful completion of this course, students should be able to:
1. Demonstrate knowledge of the emerging federated learning architecture and data privacy using different frameworks.
2. Understand the various capabilities of advanced federated learning solutions.
3. Apply state-of-the-art advanced federated learning systems to build scalable solutions for data privacy in different application domains.
4. Demonstrate the ability to build complex federated learning pipelines that integrate different architectures for dealing with heterogeneous data sets and models.
5. Develop strategic thinking and soft skills for industry and business success using cutting-edge solutions that are expected to lead the next decade of research.

Teachers:

Feras Mahmoud Naji Awaysheh - Lecturer of Edge Analytics. PhD holder. 8 years of working experience in academia.

Content:

Topic	Credit	Lectors
1. Introduction to Machine Learning (ML pipelines). -ML Lifecycle -Prepare Dataset and apply traditional ML and DL -Compare to deep learning -Data privacy and Data Protection Regulation (e.g., GDPR) -Introduction to Federated Machine learning -Simulating a vanilla FL -Apply FedAVG algorithm -Challengers of FL -FL aggregation algorithms and applications -Different FL aggregation algorithms -Horizontal and Vertical Data distribution -Guest presentation on vertical FL -Intro to FL open-source frameworks (e.g., FEDn and FLOWER) -Frameworks installation, configuration, and setup -Guest tutorial by FLOWER framework developers -FL Architectures and Communication efficiency techniques -Use cases: cross-silo and cross-device -Group read and project ideas -One-Shot Federated Learning -New trends in FL and Personalized modelling -Guest session on Federated Learning for Tabular Data -New trends e.g., Meta Learning, Transfer Learning, Split Learning, and Interactive Learning -AutoML as a solution for FL optimization -Guest session on AutoML -Applying AutoML to FL	78