08-28, 10:30–11:00 (Europe/Berlin), Room 7
In this talk, we'll review the landscape of open-source federated learning libraries with a lens on actual real world data problems, use cases and actors who could benefit from federated learning. We'll then analyze gaps, weaknesses and explore new ways we could formulate federated learning problems (and their associated libraries!) to build more useful software and use decentralized machine learning in real world use cases.
This talk will introduce:
- Aspects of federated learning that are important for real world use cases
- Federated learning libraries available via open-source
- An evaluation of federated learning open-source libraries
- A gap analysis of potential problems when leveraging open-source for real world use cases
- Suggestions for navigating this gap and building supporting libraries or new open-source solutions to address the discovered problems
Federated learning is having a renaissance, but are we solving real world problems in a way that empowers and informs those taking part? Let's explore what could help and which OSS might be most applicable!
Category [Machine and Deep Learning] –ML Applications (e.g. NLP, CV)
Expected audience expertise: Domain –some
Expected audience expertise: Python –none
Katharine Jarmul is a privacy activist, author of Practical Data Privacy and data scientist in Berlin, Germany. Her newsletter on privacy, Probably Private, covers the intersection of privacy, mathematics, probability and machine learning / data science.