Federated Optimization over Bandwidth-Limited Heterogeneous Networks

利用从大量地理分布和异构设备收集的数据的力量，而不移动数据和侵犯隐私，在推进科学技术和提高生活质量方面具有巨大的潜力。联邦优化是实现这一愿景的核心，包括训练大规模机器学习或人工智能模型，提供有洞察力的数据分析，以及在不确定性下促进决策等问题，所有这些都是以分布式方式进行的。在与带宽受限的异构网络（如物联网、智能医疗和边缘计算）对接时，联邦优化的算法基础存在显著差距，以应对在不牺牲效率的情况下驯服异构性、隐私和不确定性的独特挑战。这一研究项目还将通过提供新课程、在研究项目中指导各级学生，包括代表性不足的少数民族和妇女，并在适当的会议和讲习班上传播研究成果，与教育和劳动力发展紧密结合。该研究计划的目标是通过设计通信高效，计算可扩展和隐私保护的算法来开发一个联邦优化框架，以学习和决策，这些算法可证明在高度异构的数据和计算环境中收敛。利用机器学习，优化理论，信号处理和差分隐私的见解，该研究计划提供了一套全新的理论和算法工具，以在带宽限制下在联邦环境中实现异质性拥抱和隐私保护学习和决策，揭示计算，通信，隐私和效用之间的基本权衡。该研究计划将围绕适合满足带宽有限异构网络的各种需求的半分散式联邦设置，并专注于开发带宽有限的联邦优化算法，这些算法是高效，弹性和私有的，具有严格的性能保证，适用于机器学习，数据分析，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。

Harnessing the power of data collected from a vast amount of geographically distributed and heterogeneous devices, in a manner without moving data around and violating privacy, has great potential in advancing science and technology and improving quality of life. Federated optimization lies at the heart of the practice realizing this vision, encompassing problems such as training large-scale machine learning or artificial intelligence models, delivering insightful data analytics, as well as facilitating decision making under uncertainty, all in distributed manners. There is a significant gap in the algorithmic foundation of federated optimization when interfacing with bandwidth-limited heterogeneous networks, such as internet-of-things, smart healthcare, and edge computing, to meet the unique challenges of taming heterogeneity, privacy, and uncertainty without sacrificing efficiency. This research project will also be tightly integrated with education and workforce developments, through offering new courses, mentoring students at all levels in research projects including underrepresented minorities and women, and disseminating the research outcomes at suitable conferences and workshops. The goal of the research program is to develop a federated optimization framework to learning and decision making by designing communication-efficient, computation-scalable, and privacy-preserving algorithms that converge provably over highly heterogeneous data and computing environments. Leveraging insights from machine learning, optimization theory, signal processing, and differential privacy, the research program offers an entirely new suite of theoretical and algorithmic tools to enable heterogeneity-embracing and privacy-preserving learning and decision making in federated environments under bandwidth constraints, unveiling fundamental trade-offs among computation, communication, privacy, and utility. The research program will gravitate around a semi-decentralized federated setting suitable to meet the diverse needs of bandwidth-limited heterogeneous networks, and focus on developing bandwidth-limited federated optimization algorithms that are efficient, resilient, and private with rigorous performance guarantees for a wide range of problems arising from machine learning, data analysis, and sequential decision making.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.