Description:
Description:

This LPS event will be a hands-on workshop focusing on showcasing software solutions that can enable several innovative computing approaches: Privacy-Preserving Machine Learning, Confidential Computing, and Decentralized / Federated AI. Co-organized with DLR, Fetch.ai, DEIMOS, Scontain and T-Systems it will focus on demonstration of the specific use cases in EO data analytics (and in other industry sectors) where these new ML approaches have been successfully applied. The workshop requires bringing own laptop.

Lunch will be served.

Part 1: Scontain & T-Systems demo (Christoph Fetzer, Scontain, Rene Weinhold, T-Systems)
• Confidential computing for privacy-preserving machine learning (Scontain)
• Digital Trust, Security, Data Privacy & Blockchain (T-Systems)

Part 2: Fetch.AI demo (Emma Smith, Fetch.AI), Cedric Van Duffel (Fetch.AI)
• Federated distributed cloud, PPML and blockchain

This hands-on tutorial will follow the sessions C1.01 on Trusted Machine Learning and Agora Rountable “Privacy Preserving Technologies and Federated Machine-Learning/AI” to demonstrate how to define and run multi-stakeholder, privacy-preserving ML applications and apply new technologies to enable secure and distributed data networks and decentralised machine learning. Several use cases will be shown including early proof of concepts for EO data architectures and analytics frameworks.