Collaborative Research: SHF: Medium: Towards Harmonious Federated Intelligence in Heterogeneous Edge Computing via Data Migration

协作研究：SHF：中：通过数据迁移实现异构边缘计算中的和谐联邦智能

• 批准号: 2312617
• 负责人: Xiaonan Zhang
• 金额: $ 30万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312617&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Towards

Edge computing has promoted a plethora of emerging applications that benefit people's daily life, such as smart cities, advanced manufacturing, and connected health. As the key enabler to this promising paradigm, the widely adopted Federated Learning (FL) algorithms can reshape the edge computing by offloading the training of large-scale data to nearby edges. To achieve federated intelligence with high accuracy and high efficiency, a major hindrance is data and system heterogeneity. When deploying FL algorithms to a practical edge computing system, the collected raw data may be corrupted and participating edges may experience different computational loads. Those heterogeneity issues significantly degrade the training efficiency and accuracy for achieving the ideal system performance. This project develops a Harmonious Federated Intelligence framework to allocate the collected data to its most favorable edge for training based on its intrinsic characteristics and required hardware resources. By enabling data migration across nearby heterogeneous edges, both learning models and heterogeneous data can be fed to the optimal edge for training without either wasting or overly exploiting hardware resources. The software-hardware co-design of harmonious federated intelligence fully unleashes the computational and communication potential of exiting edge computing infrastructures in transportation systems, manufacturing industries, home automation, and connected healthcare. This project seeks to broaden the scientific view of undergraduates and underrepresented students in the field of edge computing, machine learning, and data compression, and prepare them with the cross-disciplinary skills needed to succeed in the modern workforce.By introducing data-system-algorithm harmony, this project innovates the federated learning in heterogeneous edge computing to fundamentally tackle the data heterogeneity and unbalanced hardware resources usage. Given heterogeneous data samples with imbalanced feature spaces, Thrust 1 develops an imputation-based approach to complement missing features and values. Thrust 2 designs a Parallel Grow-and-Prune sparse training framework to schedule the sparse topology of learning models with joint consideration of both hardware resource budget and data characteristics. To enable efficient data migration, Thrust 3 develops adaptive data compression schemes, including both lossy and lossless compression algorithms, in different hardware settings. Thrust 4 proposes a fine-grained control mechanism for semi-asynchronous Vertical Federated Learning to adapt hardware resource reallocation and data migration, in order to minimize the impact of individual edge staleness due to system heterogeneity. The software-hardware co-design will be evaluated through data-driven simulation and experimental validation using an integrated platform consisting of a variety of edge devices featuring diverse computation and communication capabilities. To further validate the scalability, the team will develop a large-scale prototype on the NSF FABRIC testbed with core and edge nodes across the US.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.

边缘计算催生了智慧城市、先进制造、互联健康等众多惠及人们日常生活的新兴应用。作为这一有希望的范式的关键推动者，广泛采用的联邦学习（FL）算法可以通过将大规模数据的训练卸载到附近的边缘来重塑边缘计算。要实现高精度、高效率的联邦智能，一个主要障碍是数据和系统的异构性。当将 FL 算法部署到实际的边缘计算系统时，收集的原始数据可能会损坏，并且参与的边缘可能会经历不同的计算负载。这些异构性问题显着降低了实现理想系统性能的训练效率和准确性。该项目开发了一个和谐联邦智能框架，根据其内在特征和所需的硬件资源，将收集到的数据分配到最有利的边缘进行训练。通过支持跨附近异构边缘的数据迁移，学习模型和异构数据都可以馈送到最佳边缘进行训练，而不会浪费或过度利用硬件资源。和谐联邦智能的软硬件协同设计充分释放了交通系统、制造业、家庭自动化和互联医疗保健领域现有边缘计算基础设施的计算和通信潜力。该项目旨在拓宽本科生和代表性不足的学生在边缘计算、机器学习和数据压缩领域的科学视野，并为他们提供在现代劳动力中取得成功所需的跨学科技能。算法和谐，该项目创新了异构边缘计算中的联邦学习，从根本上解决数据异构性和硬件资源使用不平衡的问题。鉴于特征空间不平衡的异构数据样本，Thrust 1 开发了一种基于插补的方法来补充缺失的特征和值。 Thrust 2设计了一个并行增长和剪枝稀疏训练框架，结合考虑硬件资源预算和数据特征来调度学习模型的稀疏拓扑。为了实现高效的数据迁移，Thrust 3 在不同的硬件设置中开发了自适应数据压缩方案，包括有损和无损压缩算法。 Thrust 4提出了一种半异步垂直联邦学习的细粒度控制机制，以适应硬件资源重新分配和数据迁移，以尽量减少由于系统异构性而导致的个体边缘陈旧的影响。软件-硬件协同设计将通过数据驱动的仿真和实验验证进行评估，使用由具有不同计算和通信功能的各种边缘设备组成的集成平台。为了进一步验证可扩展性，该团队将在 NSF FABRIC 测试平台上开发一个大型原型，核心节点和边缘节点遍布美国各地。该奖项反映了 NSF 的法定使命，通过使用基金会的智力优势和能力进行评估，被认为值得支持。更广泛的影响审查标准。