Event

Federated Learning Workshop

The Federated Learning Workshop is an event designed to bring together leading researchers, industry experts, and innovators in the field of privacy-preserving machine learning.

Organised by Wageningen Food Safety Research
Date

Mon 18 November 2024 13:00 to 16:00

Venue Phenomea, building number 125
Bornse Weilanden 9
125
6708 WG Wageningen

In an era where data collection is at an all-time high, understanding how to optimize its use while preserving privacy is more critical than ever. This workshop aims to foster collaboration, share cutting-edge research, and explore practical applications that are transforming various industries.

Why attend?

  • Expert insights: hear from international and Wageningen researchers as they share their latest findings and case studies, highlighting the connection between their fields and federated learning.
  • Networking opportunities: Connect with peers from academia and industry, fostering new collaborations and partnerships
  • Practical applications: Discover how federated learning can be applied in real-world scenarios.
  • Interactive Sessions: Engage in interactive discussions, and Q&A sessions to deepen your understanding and explore new ideas.

Keynote Speakers

  • Zuzanna Fendor (Wageningen Food Safety Research): Federated Learning in food research
  • Jasper Engels (Biometris): A case study: Federated learning and Food Authenticity
    Lan Wassenaer (Wageningen Economics Research): Digital innovation ecosystems in agrifood
  • Ramin Nikzad-Langerodi (Software Competence Center Hagenberg): Introduction to Privacy-Preserving Partial Least Squares regression
  • Osman Mutlu (Wageningen Food Safety Research): Measuring privacy when federated learning meets explainable AI
  • Afsana Khan (Maastricht University): Vertical federated learning

Moderated interactive session

During this session we discuss challenges of federated learning in our different domains.

Registration

To secure your spot, please register here by November 15, 2024.