Cross-device Federated Analytics (FA) is a distributed computation paradigm designed to answer analytics queries about and derive insights from data held locally on users' devices. On-device computations combined with other privacy and security measures ensure that only minimal data is transmitted off-device, achieving a high standard of data protection. Despite FA's broad relevance, the applicability of existing FA systems is limited by compromised accuracy; lack of flexibility for data analytics; and an inability to scale effectively. In this paper, we describe our approach to combine privacy, scalability, and practicality to build and deploy a system that overcomes these limitations. Our FA system leverages trusted execution environments (TEEs) and optimizes the use of on-device computing resources to facilitate federated data processing across large fleets of devices, while ensuring robust, defensible, and verifiable privacy safeguards. We focus on federated analytics (statistics and monitoring), in contrast to systems for federated learning (ML workloads), and we flag the key differences.

翻译：跨设备联邦分析（FA）是一种分布式计算范式，旨在对存储在用户设备本地的数据进行查询分析并从中获取洞察。设备端计算结合其他隐私与安全措施，确保仅有极少量的数据传输至设备外，从而实现高标准的数据保护。尽管FA具有广泛的应用前景，但现有FA系统的实用性受限于以下因素：精度受损、数据分析灵活性不足以及无法有效扩展。本文阐述了我们如何整合隐私性、可扩展性与实用性，以构建并部署一个能克服这些局限的系统。我们的FA系统利用可信执行环境（TEE），并优化设备端计算资源的使用，以促进跨大规模设备群的联邦数据处理，同时确保强健、可论证且可验证的隐私保护机制。与专注于联邦学习（机器学习工作负载）的系统不同，本文聚焦于联邦分析（统计与监测）领域，并明确了二者的核心差异。