在计算连续体（CC）上部署分层联邦学习（HFL）流水线，需要将参与者组织成包含 FL 客户端与全局 FL 服务器之间中间聚合节点的分层结构。由于成本约束、数据分布的变化以及 CC 易变的运行环境，实现这一点极具挑战性。为应对这些挑战，我们提出了一个用于自适应编排 HFL 流水线的框架，该框架能够响应客户端流失和基础设施级事件，同时平衡通信成本与 ML 模型准确率。我们的机制基于多级监控信息（模型准确率、资源可用性、资源成本），识别并对运行时引发 HFL 重配置操作的事件做出反应。此外，该框架引入了一种估算重配置成本的通用方法，以持续重新评估适应操作的质量，并且可扩展以优化各种 HFL 性能指标。通过扩展 Kubernetes 生态系统，我们的框架展示了对运行环境变化做出快速有效响应的能力，能够充分利用可用的通信成本预算，并在运行时有效平衡成本与 ML 性能。
英文摘要：
Deploying a Hierarchical Federated Learning (HFL) pipeline across the computing continuum (CC) requires careful organization of participants into a hierarchical structure with intermediate aggregation nodes between FL clients and the global FL server. This is challenging to achieve due to (i) cost constraints, (ii) varying data distributions, and (iii) the volatile operating environment of the CC. In response to these challenges, we present a framework for the adaptive orchestration of HFL pipelines, designed to be reactive to client churn and infrastructure-level events, while balancing communication cost and ML model accuracy. Our mechanisms identify and react to events that cause HFL reconfiguration actions at runtime, building on multi-level monitoring information (model accuracy, resource availability, resource cost). Moreover, our framework introduces a generic methodology for estimating reconfiguration costs to continuously re-evaluate the quality of adaptation actions, while being extensible to optimize for various HFL performance criteria. By extending the Kubernetes ecosystem, our framework demonstrates the ability to react promptly and effectively to changes in the operating environment, making the best of the available communication cost budget and effectively balancing costs and ML performance at runtime.
Department of Computer, Control and Management Engineering, Sapienza University of Rome（罗马萨皮恩扎大学计算机、控制与管理工程系）；Department of Computer Science, Sapienza University of Rome（罗马萨皮恩扎大学计算机科学系）；Università della Svizzera Italiana（意大利瑞士大学）
Towards Scalable Wireless Federated Learning: Challenges and Solutions
ShanghaiTech University（上海科技大学）；Nanjing University（南京大学）；Beihang University（北京航空航天大学）；Xiameng University（厦门大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Intelligent Data-Driven Architectural Features Orchestration for Network Slicing
Universidade Federal de Viçosa (UFV)（维索萨联邦大学）；Universidade Federal de Uberlândia (UFU)（乌贝兰迪亚联邦大学）；Universidade do Minho（米尼奥大学）；Universidade de São Paulo (USP)（圣保罗大学）；Universidade Salvador (UNIFACS)（萨尔瓦多大学）
Towards Scalable Federated Container Orchestration: The CODECO Approach
fortiss GmbH（fortiss有限公司）；Red Hat（红帽）；IBM Research Europe - Zurich（IBM欧洲苏黎世研究中心）；Siemens AG（西门子股份公司）；i2CAT Foundation（i2CAT基金会）；Telefónica（西班牙电信）；Intracom Telecom（Intracom电信）；Democritus University of Thrace / Athena Research Center（色雷斯德谟克利特大学/雅典研究中心）；Universidad Politécnica de Madrid（马德里理工大学）；University of Piraeus Research Centre（比雷埃夫斯大学研究中心）；Netcompany；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving
The University of Hong Kong（香港大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Damo Institute, Alibaba（阿里巴巴达摩院）；the University of New South Wales（新南威尔士大学）
Principles and Components of Federated Learning Architectures
Research and Development Department（研发部）；University of Hertfordshire（赫特福德大学）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；Westcliff university（韦斯特克莱夫大学）；Clark Atlanta University（克拉克亚特兰大大学）
Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification
Institute of Communications Engineering, National Tsing Hua University（国立清华大学通信工程研究所）；University of Electronic Science and Technology of China（电子科技大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Yonsei University（延世大学）
FOCUS: Dealing with Label Quality Disparity in Federated Learning
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Nanyang Technological University（南洋理工大学）；Xuanwu Hospital, Capital Medical University（首都医科大学宣武医院）
Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputers
Mathematics and Computer Science Division, Argonne National Laboratory（阿贡国家实验室数学与计算机科学 division）；Data Science and Learning Division, Argonne National Laboratory（阿贡国家实验室数据科学与学习 division）；Department of Computer Science, University of Chicago（芝加哥大学计算机科学系）
ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme
Lebanese American University（黎巴嫩美国大学）；Concordia University（康考迪亚大学）；New York University（纽约大学）；Zayed University（扎耶德大学）；Khalifa University（哈利法大学）；Mohammad Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Secure Multi-Modal Data Fusion in Federated Digital Health Systems via MCP
School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology, Thammasat University（泰国朱拉安吞国际技术学院，泰国 Thammasat 大学）
Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks
London South Bank University（伦敦南岸大学）；Instituto de Telecomunicações（电信研究所）
FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy
School of Computing Technologies, RMIT University（RMIT大学计算技术学院）；Swinburne University of Technology（斯威本科技大学）；Department of Computing, Macquarie University（麦考瑞大学计算系）
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning
Department of Computing, Imperial College London（帝国理工学院 computing 部）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程系）
Google Research（谷歌研究院）；University of Southern California（南加州大学）；INRIA（法国国家信息与自动化研究所）；University of Oulu（奥卢大学）；Princeton University（普林斯顿大学）；University of Warwick（华威大学）；Georgia Institute of Technology（佐治亚理工学院）；Rutgers University（罗格斯大学）；University of Virginia（弗吉尼亚大学）；University of Washington（华盛顿大学）；Carnegie Mellon University（卡内基梅隆大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Pittsburgh（匹兹堡大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of California San Diego（加利福尼亚大学圣迭戈分校）
