Federated learning (FL) deployed over quantum enabled and heterogeneous classical networks faces significant performance degradation due to uneven client quality, stochastic teleportation fidelity, device instability, and geometric mismatch between local and global models. Classical aggregation rules assume euclidean topology and uniform communication reliability, limiting their suitability for emerging quantum federated systems. This paper introduces A2G (Adaptive Aggregation with Two Gains), a dual gain framework that jointly regulates geometric blending through a geometry gain and modulates client importance using a QoS gain derived from teleportation fidelity, latency, and instability.
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
Department of Computer & Information Science & Engineering（计算机与信息科学与工程系）；University of Florida（佛罗里达大学）；Frost Institute for Data Science and Computing（数据科学与计算弗罗斯特研究所）；University of Miami（迈阿密大学）
Noise-Resilient Quantum Aggregation on NISQ for Federated ADAS Learning
Research Scholar, Dept. of CSE（研究学者，计算机科学与工程系）；Dept. of CSE, School of Engineering（计算机科学与工程系，工程学院）；PES University, RR Nagar, Bangalore, India（PES大学）；Dayananda Sagar University, Bangalore, India（Dayananda Sagar大学）
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
Zhejiang University（浙江大学）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；Westlake University（西湖大学）；Xiamen University（厦门大学）
Institute for Digital Technologies, Loughborough University（拉夫堡大学数字技术研究所）
Federated learning has gained popularity as a solution to data availability and privacy challenges in machine learning. However, the aggregation process of local model updates to obtain a global model in federated learning is susceptible to malicious attacks, such as backdoor poisoning, label-flipping, and membership inference. Malicious users aim to sabotage the collaborative learning process by training the local model with malicious data. In this paper, we propose a novel robust aggregation approach based on recursive Euclidean distance calculation. Our approach measures the distance of the…
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
National University of Singapore（新加坡国立大学）；Northeastern University（东北大学）；University of Technology Sydney（悉尼科技大学）；Tsinghua University（清华大学）；ETH Zurich（苏黎世联邦理工学院）
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model
Key Laboratory of Beijing Network Technology, Beihang University（北京网络技术重点实验室，北京航空航天大学）；Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing, Guangxi Normal University（广西多源信息整合与智能处理协同创新中心，广西师范大学）；School of Computer Science and Technology, Zhengzhou University of Light Industry（轻工业学院计算机科学与技术学院）；School of Cyber Science and Technology, Beihang University（网络安全科学与技术学院，北京航空航天大学）；Shanghai Key Laboratory of Computer Software Evaluating and Testing（上海计算机软件评测测试重点实验室）
FGRPO: Federated GRPO with Adaptive Aggregation on Non-IID Data
School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；School of Mathematical Science, Peking University（北京大学数学科学学院）；School of Computer Science and Artificial Intelligence, Shanghai University of Finance and Economics（上海财经大学计算机科学与人工智能学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai（人工智能与未来网络研究院，北京师范大学，珠海）；School of Computer Science and Artificial Intelligence, Zhengzhou University（计算机科学与人工智能学院，郑州大学）；School of Software, Nanchang University（软件学院，南昌大学）
