Federated Learning in Practice: Reflections and Projections
This work tackles practical issues in FL for organizations and researchers, but it is incremental as it builds on existing systems and trends.
The paper addresses challenges in Federated Learning (FL), such as verifying privacy guarantees and coordinating heterogeneous devices, and proposes a redefined framework prioritizing privacy principles and leveraging trusted execution environments for future advancements.
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.