CNS Core: Small: FLINT: Robust Federated Learning for Internet of Things

联邦学习使机器能够在分布式数据集上学习，而不需要学习者直接访问各自涉众拥有的数据集。物联网（IoT）为应用联合学习提供了肥沃的土壤，分布式物联网设备产生了大量通常是私有的数据。然而，物联网设备容易受到数据样本不准确和恶意攻击的环境的影响，这对联邦学习来说是一个重大挑战。以联合和健壮的方式聚合数据可以为经济和社会带来好处。物联网鲁棒联邦学习（FLINT）项目的目标包括：(1)在具有不可靠和敌对客户端的异构、动态物联网环境中制定联邦学习（FL）。(2)设计新的FL算法，该算法对良性、不可靠和恶意客户端注入错误或有毒数据的敌对条件具有鲁棒性。(3)设计新颖的激励机制，确保理性客户通过贡献培训数据和资源获得非负效用。(4)分析所提出算法的复杂度、性能和理论界限。(5)搭建物联网测试平台，研究鲁棒FL解决方案的学习性能。(6)在真实数据集上进行仿真实验，评估性能可扩展性。FLINT项目将为研究生和本科生提供一个独特的机会，让他们在物联网健壮的FL算法设计方面获得跨学科教育。研究成果将丰富物联网的网络物理系统和机器学习课程。该项目将吸引女性和代表性不足的少数民族学生，并吸引科学、技术、工程和数学（STEM）学科的中小学生。研究成果的传播将通过项目网站、研讨会和主题演讲、会议演讲、在顶级期刊和会议论文集上发表文章等方式进行。FLINT项目网站（https://tluocs.github.io/FLINT/）将在项目结束后的两年内保存计算代码、模型、真实世界和实验数据。该网站将提供两种访问级别——公共和登录要求——并且可以通过注册免费创建一个帐户。随附的GitHub存储库将使开发的代码和仿真模型可供社区使用。允许使用和自由分发数据和代码，并适当承认版权声明和作者。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。

Federated learning enables machine learning on distributed datasets without needing the learner to access directly the datasets owned by respective stakeholders. The Internet of Things (IoT) provides a fertile ground for applying federated learning, where distributed IoT devices produce a plethora of data that are often private. However, IoT devices are vulnerable to environments with inaccurate data samples and malicious attacks, which is a significant challenge for federated learning. Agglomerating data in a federated and robust manner may produce benefits to the economy and society.Objectives of the Robust Federated Learning for Internet of Things (FLINT) project include: (1) Formulate federated learning (FL) in heterogeneous, dynamic IoT environments with unreliable and adversarial clients. (2) Design new FL algorithms that are robust against hostile conditions with benign, unreliable, and malicious clients injecting erroneous or poisonous data. (3) Design novel incentive mechanisms to ensure rational clients gain non-negative utility by contributing training data and resources. (4) Analyze complexity, performance, and theoretical bounds of proposed algorithms. (5) Build an IoT testbed to study the learning performance of robust FL solutions. (6) Simulation experiments on real-world datasets to evaluate performance scalability. The FLINT project will offer graduate and undergrad students a unique opportunity to gain interdisciplinary education in the design of robust FL algorithms for IoT. Research findings will enrich courses on cyber-physical systems and machine learning for IoT. The project will attract women and underrepresented minority students, and attract primary and secondary school students in Science, Technology, Engineering and Mathematics (STEM) disciplines. Dissemination of research results will be by means of the project website, seminars and keynote talks, conference presentations, and publications in top-tier journals and conference proceedings.The FLINT project website (https://tluocs.github.io/FLINT/) will maintain computational codes, models, real world and experimental data for two years after the project period is over. The website will provide two levels of access – Public and Login required – and an account can be created via registration with no charge. An accompanying GitHub repository will make the developed codes and simulation models available to the community. Permission will be granted to use and distribute freely the data and code with due acknowledgement of the copyright notice and the authors.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.