Privacy-Preserving Federated Learning

Advancing trustworthy AI through federated learning frameworks that enable collaboration without centralizing sensitive data

A major focus of my recent work has been the development of privacy-preserving federated learning (FL) frameworks that enable collaborative AI model development without centralizing sensitive data. I led the creation of federated learning capabilities that integrate differential privacy, secure aggregation, and policy-driven governance.

Key Innovation

By allowing models to be trained directly where data reside, this work has enabled new forms of collaboration while reducing privacy risks and improving trustworthiness of AI systems. These methods have been applied to biomedical and clinical data, where privacy, bias, and model robustness are critical concerns.

Software & Services

APPFL - Argonne Privacy Preserving Federated Learning

Open-source framework with 141+ pull requests, 38 GitHub stars, and active community contributions. Successfully applied across diverse scientific fields from smart grid to COVID prediction.

APPFLx - Federated Learning as a Service

Privacy-preserving platform deployed on AWS, used by NIH-funded Bridge2AI and multiple research groups.

Impact

This contribution provides a practical foundation for deploying federated AI in real-world biomedical and healthcare settings, including national lab, VA, and academic collaborations.

Resources