TSC 2025/02/06 (Federated Learning)

Topic: Introduction to EdgeLake & Federated Learning

AI at the edge is transforming industries, but traditional cloud-based models introduce latency, security risks, and high costs. EdgeLake enables a federated learning approach that keeps data local, ensuring real-time processing, privacy, and efficiency.

This session explores why federated learning is crucial for AI at the edge, the key challenges limiting adoption, and how EdgeLake bridges deployment gaps. We’ll wrap up with a live demo showcasing EdgeLake in action, followed by an open discussion on next steps.

Join us for a live demo showcasing how EdgeLake enables Federated Learning at the Edge, fully automating the data lifecycle while ensuring privacy and efficiency.

This session will demonstrate a real-world use case.

Agenda:

1️⃣ Introduction to EdgeLake & Federated Learning – Why it matters for AI at the edge
2️⃣ Current Challenges in Federated Learning – Key obstacles limiting adoption
3️⃣ Closing the Loop – Addressing deployment gaps in Federated Learning
4️⃣ Live Demo – Practical implementation using EdgeLake
5️⃣ Conclusion & Q&A – Open discussion and next steps

Meeting recordings

Meeting Deck

Open