Basics of federated learning. Tips and tricks

• Khadijeh Alibabaei (KIT)

Begin your journey into federated learning with a comprehensive overview of the basics, accompanied by invaluable tips and tricks for success. Explore the principles behind federated learning, including its decentralized nature and collaborative model, and gain practical insights into optimizing model performance and mitigating common challenges. Whether you're a novice or seasoned practitioner, this session equips you with the foundational knowledge and strategies needed to excel in federated learning.

Understanding threats in federated learning

• Alberto Pedrouzo-Ulloa (atlanTTic Research Center, Universidade de Vigo)

Enter into the complexities of federated learning security as we uncover potential threats and vulnerabilities inherent in decentralized machine learning environments. From data privacy concerns to examples of privacy attacks by malicious adversaries, this session explores the challenges of securing federated learning systems. Gain insights into how to adequately measure privacy for safeguarding sensitive data and acquire a deeper understanding of the privacy threats present in federated learning.

Flower: a friendly federated learning framework

• Javier Fernandez-Marques (Flower Labs)

Discover Flower, a user-friendly federated learning framework designed to simplify the development and deployment of distributed machine learning models. Learn about Flower's architecture, features, and capabilities, and explore how it streamlines the implementation of federated learning algorithms across diverse environments.

Federated learning in the AI4EOSC platform (demo)

• Judith Sainz-Pardo Diaz (CSIC)

Experience federated learning in action with a live demonstration showcasing its integration within the AI4EOSC platform. Explore real-world use cases and scenarios where federated learning enhances data collaboration, privacy preservation, and model scalability within the European Open Science Cloud (EOSC) ecosystem. Through hands-on examples and demonstrations, uncover how Flower and AI4EOSC empower researchers and developers to unlock the full potential of federated learning in collaborative settings.