Federated Analytics in Practice: Engineering for Privacy, Scalability and Practicality

• URL: http://arxiv.org/abs/2412.02340v1
• Date: Tue, 03 Dec 2024 10:03:12 GMT
• Title: Federated Analytics in Practice: Engineering for Privacy, Scalability and Practicality
• Authors: Harish Srinivas, Graham Cormode, Mehrdad Honarkhah, Samuel Lurye, Jonathan Hehir, Lunwen He, George Hong, Ahmed Magdy, Dzmitry Huba, Kaikai Wang, Shen Guo, Shoubhik Bhattacharya,
• Abstract summary: 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. 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. We describe our approach to combine privacy, scalability, and practicality to build and deploy a system that overcomes these limitations.
• Score: 5.276674920508729
• License:
• Abstract: 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.

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