DINS+LERSAIS+PittCyber Seminar

Advanced Privacy-Preserving Federated Learning as a Service (APPFLx): Building Robust and Trustworthy AI Models

Ravi K. Madduri, Senior Computer Scientist
Data Science and Learning Division
Argonne National Laboratory

Refreshments will be served.

Abstract: Conducting secure privacy-preserving federated learning (PPFL) experiments that leverage large-scale, high-performance computational resources across distributed sites often demands technical capabilities beyond the reach of many organizations. To lower the barrier to entry for PPFL and empower domain experts in large institutions to utilize federated learning (FL), we developed Advanced Privacy-Preserving Federated Learning as a Service (APPFLx). APPFLx simplifies cross-silo PPFL through an intuitive web interface for managing, deploying, analyzing, and visualizing FL experiments.

The platform ensures secure federations using robust, end-to-end Identity and Access Management. Participants can establish or join federations using institutional credentials, perform privacy-preserving training on local datasets, and securely share model updates for aggregation. Additionally, we present the AI Data Readiness Inspector (AIDRIN) framework, designed to assess dataset readiness for AI applications, incorporating both traditional data quality metrics and AI-specific considerations. We discuss how integrating AIDRIN with APPFLx enhances data preparation and facilitates improved FL experiments, fostering trustworthy and effective AI model development.

Bio: Ravi is a computer scientist working in the intersection of HPC/AI and biomedicine. His research interests are in building sustainable, scalable services for science, reproducible research, large-scale data management, analysis using HPC and AI. He leads the PALISADE-X project that is developing Privacy-preserving Federated Learning framework to build robust, trust-worthy AI models. He co-leads the MVP-CHAMPION project, which is a collaboration between VA and DOE and develops methods to perform large-scale genetic data analysis using DOE’s high performance computing capabilities, including methods for generating PRS scores in Prostate Cancer, genome-wide PheWAS on Summit supercomputer. Additionally, Ravi is one of three key contributors to the National Institutes of Health $100M Cancer Biomedical Informatics Grid (caBIG), which linked 60 NIH-funded cancer centers and clinical sites engaged in cancer research. For his efforts in project management, tool development, and collaboration, Ravi received several Outstanding Achievement Awards from NIH. For his work on “Cancer Moonshot” project, he received the Department of Energy Secretary award in 2017.