Collaborative Research: CNS Core: Medium: Systems Support for Federated Learning

将机器学习技术应用于最终用户数据的传统方法通常需要将所有数据复制到云中。这不仅成本高昂，而且还面临数据隐私风险。通过分析生成数据的设备上的数据，联邦学习旨在减轻集中式机器学习的成本和隐私问题，同时又不牺牲其优势。这个合作项目汇集了来自两个机构的研究人员，通过解决用户设备的多样性和这些设备中数据分布的异质性所带来的挑战，为实际的联邦学习开发构建块。该项目采用三管齐下的方法：(1)提高机器学习开发人员的性能（例如，明智地选择参与者，而不是随机选择参与者）；(2)提高服务供应商的效率（例如，消除数据传输的冗余）；(3)使最终用户能够在不牺牲设备可用性的情况下控制其数据隐私（例如，类似于Android中的应用程序权限）。支撑这些解决方案的两个核心原则是：云和单个设备上的多租户；维护联邦学习算法的理论正确性、收敛特性和隐私/安全保证。广泛采用实用的联合学习可以从根本上改变我们从最终用户数据中收集见解的方式，以及用户如何重视数据隐私，因为在许多情况下，用户可能不必为了方便而牺牲隐私。反过来，这可能会迫使大公司重新考虑他们的数据收集和使用做法，并影响政策制定者考虑更严格的隐私法规。这个项目的所有软件都将是开源的。通过外展和新的教育材料，该项目将率先培训具有隐私意识的系统建设者。这个协作项目将产生软件工件、实验工具、基准，以及运行这些基准和工件的结果。这些材料将在许可的开源许可证下在多个地点提供给公众使用，包括https://github.com/symbioticlab。它们将在项目完成后至少保留三年。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Traditional approaches toward applying machine learning techniques to end-user data often require copying all data to the cloud. This is not only expensive but faces data privacy risks as well. By analyzing data on the device where it is generated, federated learning aims to mitigate both cost and privacy concerns of centralized machine learning without sacrificing its benefits. This collaborative project brings together investigators from two institutions to develop building blocks for practical federated learning by addressing challenges arising from the diversity of user devices and the heterogeneity of data distributions in those devices. The project takes a three-pronged approach: (1) enable performance improvements for machine learning developers (e.g., judicious participant selection instead of randomly selecting participants); (2) provide efficiency improvements for service providers (e.g., redundancy elimination for data transfers); (3) enable end-users to control their data privacy (e.g., akin to app permissions in Android) without sacrificing device usability. Two core principles underpin these solutions: multi-tenancy both in the cloud and on individual devices; and maintaining theoretical correctness, convergence characteristics, and privacy/security guarantees of federated learning algorithms. Widespread adoption of practical federated learning can fundamentally change how we gather insights from end-user data and how users value data privacy, because users may not have to sacrifice privacy for convenience in many cases. This, in turn, can force large corporations to rethink their data collection and usage practices, and influence policy makers to consider stricter privacy regulations. All software from this project will be open source. Through outreach and new educational materials, this project will pioneer the training of privacy-aware systems builders.This collaborative project will produce software artifacts, experimental harnesses, benchmarks, and results of running those benchmarks and artifacts. These materials will be available for public use under permissive open-source licenses at multiple locations, including https://github.com/symbioticlab. They will be retained for at least three years after the completion of the project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.