Experiences Building Enterprise-Level Privacy-Preserving Federated Learning to Power AI for Science

2025-11-12

AI Summary

Privacy-preserving federated learning for scientific AI faces challenges in scalability, heterogeneous deployment, and usability. This paper introduces APPFL, a novel framework featuring a multi-level abstraction architecture that enables seamless transition from simulation to real-world deployment across diverse platforms—including edge devices, cloud clusters, and high-performance computing (HPC) systems. APPFL integrates differential privacy, secure aggregation, trusted execution environments (TEEs), and strong identity authentication to support large-scale collaborative training while ensuring data locality and regulatory compliance. Designed to balance research flexibility with engineering robustness, APPFL bridges the gap between academic prototypes and production-grade applications. Empirical evaluation across multiple data-sensitive scientific domains demonstrates its efficiency, security, and practical viability.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Applications
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Federated learning (FL) is a promising approach to enabling collaborative model training without centralized data sharing, a crucial requirement in scientific domains where data privacy, ownership, and compliance constraints are critical. However, building user-friendly enterprise-level FL frameworks that are both scalable and privacy-preserving remains challenging, especially when bridging the gap between local prototyping and distributed deployment across heterogeneous client computing infrastructures. In this paper, based on our experiences building the Advanced Privacy-Preserving Federated Learning (APPFL) framework, we present our vision for an enterprise-grade, privacy-preserving FL framework designed to scale seamlessly across computing environments. We identify several key capabilities that such a framework must provide: (1) Scalable local simulation and prototyping to accelerate experimentation and algorithm design; (2) seamless transition from simulation to deployment; (3) distributed deployment across diverse, real-world infrastructures, from personal devices to cloud clusters and HPC systems; (4) multi-level abstractions that balance ease of use and research flexibility; and (5) comprehensive privacy and security through techniques such as differential privacy, secure aggregation, robust authentication, and confidential computing. We further discuss architectural designs to realize these goals. This framework aims to bridge the gap between research prototypes and enterprise-scale deployment, enabling scalable, reliable, and privacy-preserving AI for science.

Problem

• Building scalable enterprise federated learning without centralized data sharing
• Bridging local prototyping and distributed deployment across infrastructures
• Ensuring comprehensive privacy through differential privacy and secure aggregation

Innovation

• Scalable local simulation for rapid prototyping
• Seamless transition from simulation to deployment
• Multi-level privacy via differential and secure aggregation