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.

本研究解决了联邦学习系统在隐私保障与设备协调方面的关键挑战，这些问题限制了联邦学习的广泛应用。通过提出一个重新定义的联邦学习框架，更加重视隐私原则，结合可信执行环境和开源生态系统，本研究为未来联邦学习的发展指明了方向。其主要发现是，强化隐私保障与多样化设备的协作能力可以显著提升联邦学习的实际应用潜力。

联邦学习实践：反思与展望