Building infra for global FL collaboration — would love your input!

Building the Foundations for Global Federated Learning Collaboration: Your Insights Needed

As the landscape of artificial intelligence continues to evolve, Federated Learning (FL) has emerged as a promising approach to train models across decentralized data sources while preserving privacy. However, despite the technical capabilities provided by frameworks such as Flower, NVFlare, and OpenFL, scaling collaborative efforts across multiple organizations remains a significant challenge.

The Vision:
Our team is developing a comprehensive coordination layer designed to facilitate cross-institutional Federated Learning that is secure, transparent, and trustless. The aim is to create infrastructure that not only supports the technical aspects but also addresses the critical issues of trust, governance, auditability, incentives, and reproducibility—barriers that often hinder large-scale deployment.

Seeking Community Insights:
If you’re actively working with Federated Learning in either research or production environments, your experience and insights could be invaluable. We invite you to spend just a couple of minutes completing a brief survey aimed at understanding the current pain points, effective strategies, and what infrastructure might accelerate FL’s adoption and impact.

Your Feedback Matters:
By sharing your perspectives, you’ll help shape solutions that enable broader, more effective collaboration in Federated Learning. Additionally, we welcome discussions and ideas in the comments, particularly around what obstacles prevent FL from becoming the default choice for AI training at scale.

Participate in the Survey:
[Link to survey in comments]

Together, we can help unlock the full potential of Federated Learning and pave the way for more secure, scalable, and trustworthy AI development.