Distributed and Federated Learning

Description:

This course is designed to extend students’ knowledge of learning in a decentralized setting. Decentralized learning techniques, such as federated learning, are set to deliver a new generation of machine learning applications by enabling efficient and reliable learning between multiple parties and from diverse data sources.  This course will cover different aspects of federated learning, focusing on recent research developments and exploring important applications in different fields such as security, networks and healthcare.

Lectures

• Course Overview. Introduction to machine learning and Federated Learning.
• Decentralized Optimization and Gradient descent
• Federated learning: FedSGD and FedAvg
• Variations of Federated Aggregation.
• Federated Averaging with Heterogeneous Data

• Communication-Efficient Learning of deep networks in Federated Learning
• Federated Multi-Task learning

Lab sessions

• Build and scale a simple federated learning with MNIST, Cifar-10, Fashion-MNIST, MedMNIST, Shakespeare, and BCN Open Data. Open-source Federated Learning tools (Pytorch, Flower, etc.).
• Federated learning with Non-IID data.

Complementary content:

• Threats, attacks, and defenses to federated learning
• Designing an attack and setting up a defense for federated learning.
• Applications to Images, Networks, health, and vehicle-to-vehicle communications
• Labs
• Applications of federated learning to network anomaly detection: use of 5G and LoRaWAN testbeds and datasets, with lab [by CNAM].
• Applications of federation learning to medical equipment: use of aggregated and anonymized field data [by NTUU].
• Applications of federation learning to vehicle-to-vehicle communications: routing and content offloading [by UPC].