Hierarchical federated learning (HFL) shows great advantages over conventional two-layer federated learning (FL) in reducing network overhead and interaction latency while still retaining the data privacy of distributed FL clients. However, the communication and energy overhead still pose a bottleneck for HFL performance, especially as the number of clients raises dramatically. To tackle this issue, we propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation in this paper, aiming to minimize the total cost of time and energy at each HFL global round. Specifically, we first propose a novel fuzzy logic based client orchestration policy considering client heterogenerity in multiple aspects, including channel quality, data quantity and model staleness. Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated. Utilizing problem decomposition, we firstly derive the closed-form solution for the edge server scheduling subproblem via the penalty dual decomposition (PDD) method. Next, a deep deterministic policy gradient (DDPG) based algorithm is proposed to tackle the resource allocation subproblem considering time-varying environments. Finally, extensive simulations demonstrate that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
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）
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）
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）
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）
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）
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）
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）
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）
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）
MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems
Department of Computer Engineering（计算机工程系）；Faculty of Engineering（工程学院）；Kırıkkale University（Kirikkkale 大学）；Natural Sciences, Sabancı University（自然科学，萨班奇大学）
