Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations
Department of Electrical Engineering (ISY), Linköping University（_linköping大学电气工程系）；CentraleSupélec, CNRS, L2S, Université Paris-Saclay（_centralesupelec, CNRS, L2S, 巴黎萨克雷大学）；Division of Decision and Control Systems, School of EECS, KTH Royal Institute of Technology（_决策与控制系统系，皇家理工学院）
Adrian Edin, Michel Kieffer, Mikael Johansson, Zheng Chen
备注：
14 pages, 7 figures, submitted for possible publication
The communication bottleneck in federated learning (FL) has spurred extensive research into techniques to reduce the volume of data exchanged between client devices and the central parameter server. In this paper, we systematically classify gradient and model compression schemes into three categories based on the type of correlations they exploit: structural, temporal, and spatial. We examine the sources of such correlations, propose quantitative metrics for measuring their magnitude, and reinterpret existing compression methods through this unified correlation-based framework. Our experimental studies demonstrate that the degrees of structural, temporal, and spatial correlations vary significantly depending on task complexity, model architecture, and algorithmic configurations. These findings suggest that algorithm designers should carefully evaluate correlation assumptions under specific deployment scenarios rather than assuming that they are always present. Motivated by these findings, we propose two adaptive compression designs that actively switch between different compression modes based on the measured correlation strength, and we evaluate their performance gains relative to conventional non-adaptive approaches. In summary, our unified taxonomy provides a clean and principled foundation for developing more effective and application-specific compression techniques for FL systems.
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
Southern Illinois University（南伊利诺伊大学）；University of Asia Pacific（亚太大学）；Green University of Bangladesh（孟加拉国绿色大学）；University of North Texas（北得克萨斯大学）；John Hopkins University（约翰霍普金斯大学）
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
Imperial College London（伦敦帝国学院）；Massachusetts General Hospital（麻省总医院）；Zhejiang University（浙江大学）；Weill Cornell Medicine of Cornell University（康奈尔大学威尔医学院）；Royal Brompton Hospital（皇家布朗普顿医院）；King’s College London（伦敦国王学院）
Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities
Vellore Institute of Technology（韦洛尔理工学院）；Charles Darwin University（查尔斯达尔文大学）；Stockholm University（斯德哥尔摩大学）；Chaitanya Bharathi Institute of Technology（柴塔尼亚巴拉蒂理工学院）
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
Department of Computer Science and Engineering, BITS Pilani Dubai Campus（计算机科学与工程系，比斯帕尼迪拜校区）；Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科技大学）
Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges
Department of Computing Science, Umeå University（计算机科学系，乌梅大学）；Division of Robotics, Perception and Learning, KTH Royal Institute of Technology（机器人、感知与学习 division，皇家理工学院）
Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification
Institute of Exact and Technological Sciences, Federal University of Viçosa - UFV, Rio Paranaíba-MG, Brazil（精确与技术科学研究所，弗拉维亚联邦大学-UFV，里奥帕拉纳伊巴-MG，巴西）；Department of Computing, Federal University of São Carlos, São Carlos-SP, Brazil（计算系，萨o卡洛斯联邦大学，萨o卡洛斯-SP，巴西）
DecHW: Heterogeneous Decentralized Federated Learning Exploiting Second-Order Information
School of Information Technology, Deakin University（德肯大学信息科技学院）；Institute for Informatics and Telematics (IIT), Italian National Research Council (CNR)（信息与电信学研究院（IIT），意大利国家研究委员会（CNR））
Communication-Efficient Federated Learning by Exploiting Spatio-Temporal Correlations of Gradients
College of Information Science and Technology, Jinan University（信息科学与技术学院，暨南大学）；College of Engineering, Mathematics and Physical Sciences, University of Exeter（工程、数学与物理科学学院，埃克塞特大学）
Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
Department of Computer Science, Universitat Autònoma de Barcelona（巴塞罗那自治大学计算机科学系）；Computer Vision Center（计算机视觉中心）；Media Integration and Communication Center (MICC), University of Florence（佛罗伦萨大学媒体整合与通信中心）；IDEAS Research Institute（IDEAS研究机构）；City University of Hong Kong（香港城市大学）；Program of Computer Science, City University of Hong Kong (Dongguan)（香港城市大学（东莞）计算机科学项目）
Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Shanghai Jiao Tong University（上海交通大学）；Nanjing University（南京大学）；The Hong Kong Polytechnic University（香港理工大学）
无人驾驶飞行器（UAV），或者说无人机，被设想为支持民用和军事领域中的下一代无线网络中的广泛应用。通过人工智能（AI）特别是机器学习（ML）技术来增强无人机网络智能是不可避免的，并且对实现上述应用具有吸引力。为了解决无人机网络中传统的以云为中心的ML的问题，例如隐私问题、不可接受的延迟和资源负担，提出了一种分布式ML技术，即\textit（即），联邦学习（FL）最近被提出来使多个UAV能够协作地训练ML模型而不泄露原始数据。然而，几乎所有现有的FL范例仍然是集中式的，\textit{i.e.}，一个中央实体负责整个网络上的ML模型聚合和融合，这可能导致单点故障的问题，并且不适合具有不可靠节点和链路的UAV网络。因此，在这篇文章中，我们提出了一种新的架构称为DFL-UN（\underline{D} ecentralized \underline{F} ederated \underline{L} earning for \underline{U} AV \underline{N} etworks），它使FL无人机网络中没有一个中央实体。我们还进行了初步的模拟研究，以验证DFL-UN架构的可行性和有效性。最后，我们讨论了DFL-UN面临的主要挑战和潜在的研究方向。
Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran（塔里斯坦大学电气与计算机工程学院）；School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran（谢尔兹大学电气与计算机工程学院）；Okinawa Institute of Science and Technology, Okinawa, Japan（冲绳科学技术研究院）；School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran（基础科学研究院（IPM）计算机科学学院）
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Arizona State University（亚利桑那州立大学）；The Hong Kong University of Science and Technology（香港科技大学）
Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions
Zhejiang A&F University（浙江农林大学）；Lovely Professional University；Trinity College Dublin（三一学院）；Ho Chi Minh City University of Education and Technology（胡志明市教育与技术大学）；Northumbria University（诺森比亚大学）；University College Dublin（都柏林大学）；University of Oulu（奥卢大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Beijing Institute of Technology（北京理工大学）；Deakin University（迪肯大学）
Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare
Institute of Informatics University of Amsterdam（信息学院阿姆斯特丹大学）；College of Medicine The Ohio State University（医学学院俄亥俄州立大学）；Department of Neuroscience University of California（神经科学系加州大学）；School of Medicine Indiana University（医学院印第安纳大学）
