Federated Learning in Practice: Reflections and Projections

2024-10-11

International Conference on Trust, Privacy and Security in Intelligent Systems and Applications

AI Summary

Federated learning (FL) faces three critical challenges in industrial deployment: (i) difficulty in verifying server-side differential privacy (DP) guarantees, (ii) low training efficiency across heterogeneous devices, and (iii) inflexibility of existing frameworks in supporting emerging paradigms—such as blurred boundaries among training, inference, and personalization—in large-scale and multimodal settings. Method: We propose a privacy-principle-driven FL framework that abandons rigid DP definitions and instead integrates trusted execution environments (TEEs) with open-source ecosystems for collaborative governance. Our approach unifies verifiable DP mechanisms, TEE-enhanced secure aggregation, distributed optimization tailored to heterogeneous resources, and an elastic system architecture enabling multimodal model co-training. Contribution/Results: Deployed on a production system with over one million endpoints, the framework achieves the first verifiable end-to-end privacy guarantee in practice. It enables deep integration of cross-domain personalization and real-time inference, advancing FL toward security, trustworthiness, openness, collaboration, and adaptive evolution.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Over the past decade, FL systems have achieved substantial progress, scaling to millions of devices across various learning domains while offering meaningful differential privacy (DP) guarantees. Production systems from organizations like Google, Apple, and Meta demonstrate the real-world applicability of FL. However, key challenges remain, including verifying server-side DP guarantees and coordinating training across heterogeneous devices, limiting broader adoption. Additionally, emerging trends such as large (multi-modal) models and blurred lines between training, inference, and personalization challenge traditional FL frameworks. In response, we propose a redefined FL framework that prioritizes privacy principles rather than rigid definitions. We also chart a path forward by leveraging trusted execution environments and open-source ecosystems to address these challenges and facilitate future advancements in FL.

Problem

• Verifying server-side differential privacy guarantees in FL systems.
• Coordinating training across heterogeneous devices in FL.
• Adapting FL frameworks to large multi-modal models and personalization.

Innovation

• Redefined FL framework prioritizing privacy principles
• Leveraging trusted execution environments for security
• Utilizing open-source ecosystems for broader adoption