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

协作研究：CNS 核心：中：联邦学习的系统支持

• 批准号: 2105890
• 负责人: Aditya Akella
• 金额: $ 40万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105890&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

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.

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。这不仅昂贵，而且还面临数据隐私风险。通过分析生成的设备上的数据，联邦学习旨在减轻集中机器学习的成本和隐私问题而无需牺牲其收益。该协作项目将来自两个机构的调查人员汇集在一起​​，通过解决用户设备多样性以及这些设备中数据分布的异质性引起的挑战来开发实用联合学习的构建障碍。该项目采用三管齐下的方法：（1）为机器学习开发人员（例如，明智的参与者选择而不是随机选择参与者）启用性能改进； （2）为服务提供商提供效率提高（例如，消除数据传输的冗余）； （3）使最终用户能够控制其数据隐私（例如，类似于Android中的应用程序权限），而无需牺牲设备可用性。这些解决方案的两个核心原则：在云和各个设备上的多租金；并保持理论正确性，收敛特征以及联合学习算法的隐私/安全保证。实用联邦学习的广泛采用可以从根本上改变我们从最终用户数据中收集见解以及用户如何重视数据隐私的方式，因为在许多情况下，用户可能不必为了方便起见而牺牲隐私。反过来，这可能迫使大公司重新考虑其数据收集和使用实践，并影响政策制定者考虑更严格的隐私法规。该项目的所有软件将是开源的。通过外展和新的教育材料，该项目将开拓隐私感知系统建筑商的培训。该协作项目将生产软件文物，实验线束，基准测试和运行这些基准测试和文物的结果。这些材料将在包括https://github.com/symbioticlab在内的多个位置的宽松开源许可下公开使用。该项目完成后将至少保留三年。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。