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

CNS 核心：小型：FLINT：物联网的鲁棒联邦学习

• 批准号: 2008878
• 负责人: Tony Luo
• 金额: $ 50万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008878&HistoricalAwards=false
• 关键词: CNS; Core; Small; FLINT; Robust

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.

联合学习使机器学习能够在分布式数据集上进行，而不需要学习者直接访问各自利益相关者拥有的数据集。物联网（IoT）为应用联邦学习提供了肥沃的土壤，分布式IoT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具有不准确数据样本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。以联邦和健壮的方式聚集数据可能会对经济和社会产生好处。物联网健壮联邦学习（FLINT）项目的目标包括：（1）在具有不可靠和对抗性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，这些算法在良性、不可靠和恶意客户端注入错误或有毒数据的恶劣条件下具有强大的鲁棒性。(3)设计新颖的激励机制，确保理性客户通过贡献训练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)构建物联网测试平台，研究强大FL解决方案的学习性能。(6)在真实世界数据集上进行模拟实验，以评估性能可扩展性。FLINT项目将为研究生和本科生提供一个独特的机会，以获得跨学科教育，为物联网设计强大的FL算法。研究结果将丰富物联网的网络物理系统和机器学习课程。该项目将吸引妇女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、会议演示以及在顶级期刊和会议论文集上发表文章的方式进行。FLINT项目网站（https：//tluocs.github.io/FLINT/）将在项目期结束后的两年内维护计算代码、模型、真实的世界和实验数据。该网站将提供两个级别的访问-公共和登录要求-和一个帐户可以通过注册免费创建。附带的GitHub存储库将向社区提供开发的代码和模拟模型。该奖项反映了NSF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。

项目成果

FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Environments

• DOI: 10.1145/3576841.3585921
• 发表时间: 2023-05
• 期刊: Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023)
• 作者: Ashish Gupta;Hari Prabhat Gupta;Sajal K. Das

FedHAP: Fast Federated Learning for LEO Constellations using Collaborative HAPs

• DOI: 10.1109/wcsp55476.2022.10039157
• 发表时间: 2022-05
• 期刊: 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)
• 作者: Mohamed Elmahallawy;Tie Luo

Robust Federated Learning against Backdoor Attackers

• DOI: 10.1109/infocomwkshps57453.2023.10225922
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
• 作者: Priyesh Ranjan;Ashish Gupta;Federico Coró;Sajal Kumar Das

Blockchain-Enabled Authenticated Key Agreement Scheme for Mobile Vehicles-Assisted Precision Agricultural IoT Networks

• DOI: 10.1109/tifs.2022.3231121
• 发表时间: 2023
• 期刊: IEEE Transactions on Information Forensics and Security
• 影响因子: 6.8
• 作者: Anusha Vangala;A. Das;Ankush Mitra;Sajal K. Das;Youngho Park

AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms

• DOI: 10.1109/bigdata55660.2022.10021101
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Mohamed Elmahallawy;Tie Luo