National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China（多媒体软件国家工程研究中心，计算机科学学院，武汉大学，武汉，中国）
Zihan Tan, Suyuan Huang, Guancheng Wan, Wenke Huang, He Li, Mang Ye
Federated Graph Learning (FGL) combines the privacy-preserving capabilities of federated learning (FL) with the strong graph modeling capability of Graph Neural Networks (GNNs). Current research addresses subgraph-FL from the structural perspective, neglecting the propagation of graph signals on spatial and spectral domains of the structure. From a spatial perspective, subgraph-FL introduces edge disconnections between clients, leading to disruptions in label signals and a degradation in the semantic knowledge of the global GNN. From a spectral perspective, spectral heterogeneity causes inconsistencies in signal frequencies across subgraphs, which makes local GNNs overfit the local signal propagation schemes. As a result, spectral client drift occurs, undermining global generalizability. To tackle the challenges, we propose a global knowledge repository to mitigate the challenge of poor semantic knowledge caused by label signal disruption. Furthermore, we design a frequency alignment to address spectral client drift. The combination of Spatial and Spectral strategies forms our framework S2FGL. Extensive experiments on multiple datasets demonstrate the superiority of S2FGL. The code is available at https://github.com/Wonder7racer/S2FGL.git.
We present a spatial-temporal federated learning framework for graph neural networks, namely STFL. The framework explores the underlying correlation of the input spatial-temporal data and transform it to both node features and adjacency matrix. The federated learning setting in the framework ensures data privacy while achieving a good model generalization. Experiments results on the sleep stage dataset, ISRUC_S3, illustrate the effectiveness of STFL on graph prediction tasks.
Indian Institute of Information Technology Kottayam (IIITK)（印度信息技术学院科塔亚姆分校（IIITK））；International Institute of Information Technology Hyderabad (IIITH)（印度信息技术学院海得拉巴分校（IIITH））
Heterogeneity-Aware Knowledge Sharing for Graph Federated Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China（南京理工大学计算机科学与工程学院）；College of Smart Agriculture, Nanjing Agricultural University, Nanjing, China（南京农业大学智能农业学院）；School of Intelligence Science and Technology, Nanjing University, Suzhou, China（南京大学智能科学与技术学院）；Department of Computer Science, Hong Kong Baptist University, Hong Kong, China（香港 Baptist 大学计算机科学系）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China（上海交通大学自动化与智能感知学院）
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Wuhan University（武汉大学）；National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；Institute of Artificial Intelligence（人工智能研究院）；School of Computer Science（计算机学院）；Taikang Center for Life and Medical Sciences（泰康生命与医学科学中心）
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for edge computation and licensing (data access) issues. While federated learning (FL) has emerged as a framework for model training without requiring direct data sharing and exchange, effectively modeling the complex spatio-temporal dependencies to improve forecasting capabilities still remains an open problem. On the other hand, state-of-the-art spatio…
FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows
Carnegie Mellon University（卡内基梅隆大学）；University of Wisconsin, Madison（威斯康星大学麦迪逊分校）；Ohio State University（俄亥俄州立大学）；University of California, Davis（加州大学戴维斯分校）
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks
College of Electronic and Information Engineering, Southwest University（西南大学电子信息工程学院）；School of Electrical Engineering and Telecommunications, the University of New South Wales（新南威尔士大学电气工程与电信学院）；Department of Electrical Engineering, City University of Hong Kong（香港城市大学电气工程系）；Department of Electrical and Electronic Engineering, the University of Hong Kong（香港大学电气与电子工程系）
Virginia Tech（弗吉尼亚理工大学）；University of Colorado（科罗拉多大学）；University of Oulu（奥卢大学）
To meet the growing mobility needs in intra-city transportation, the concept of urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as corridors, that link the aerodromes. A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace and create a fast, efficient, and safe transportation system. In this paper, to characterize the wireless connectivity performance for UAM, a spatial model is proposed. For …
Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregation
University of Chinese Academy of Sciences（中国科学院大学）；Tianjin University（天津大学）；University of Science and Technology of China（中国科学技术大学）；University of Rochester（罗切斯特大学）；State Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering（信息工程研究所网络空间安全防御国家重点实验室）
University of Washington（华盛顿大学）；Kennesaw State University（肯尼索州立大学）
Federated learning involves training statistical models over remote devices such as mobile phones while keeping data localized. Training in heterogeneous and potentially massive networks introduces opportunities for privacy-preserving data analysis and diversifying these models to become more inclusive of the population. Federated learning can be viewed as a unique opportunity to bring fairness and parity to many existing models by enabling model training to happen on a diverse set of participants and on data that is generated regularly and dynamically. In this paper, we discuss the current me…
SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）
Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
Southwestern University of Finance and Economics（西南财经大学）；University of Electronic Science and Technology of China（电子科技大学）；WeBank（微众银行）；Southwest Jiaotong University（西南交通大学）；Hong Kong University of Science and Technology（香港科技大学）
Clustering in graphs has been a well-known research problem, particularly because most Internet and social network data is in the form of graphs. Organizations widely use spectral clustering algorithms to find clustering in graph datasets. However, applying spectral clustering to a large dataset is challenging due to computational overhead. While the distributed spectral clustering algorithm exists, they face the problem of data privacy and increased communication costs between the clients. Thus, in this paper, we propose a spectral clustering algorithm using federated learning (FL) to overcom…
CTTC（加泰罗尼亚电信技术中心）；Trinity College Dublin（都柏林圣三一学院）；Imperial College London（伦敦帝国理工学院）；New York University（纽约大学）；University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）；Xiamen University（厦门大学）；Universitat Pompeu Fabra（庞培法布拉大学）
As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency. To unleash the potential of such new features, artificial intelligence (AI) and machine learning (ML) are currently being exploited for deriving models and protocols from data, rather than by hand-programming. In this paper, we explore the feasibility of applying ML in next-generation wireless local area networks (WLANs). More specifically, we focus on the IEEE 802.11ax spatial reuse (SR) problem and predict its performance …
Spatial-Temporal Federated Learning for Lifelong Person Re-identification on Distributed Edges
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
Urmia University of Technology（乌尔米耶技术大学）；Islamic Azad University, Central Tehran Branch（伊斯兰阿扎德大学中央德黑兰分校）；Universitat Rovira I Virgili（罗维拉-威尔吉利大学）
Machine learning models based on sensitive data in the real-world promise advances in areas ranging from medical screening to disease outbreaks, agriculture, industry, defense science, and more. In many applications, learning participant communication rounds benefit from collecting their own private data sets, teaching detailed machine learning models on the real data, and sharing the benefits of using these models. Due to existing privacy and security concerns, most people avoid sensitive data sharing for training. Without each user demonstrating their local data to a central server, Federate…
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
Department of XXX, University of YYY, Location, Country（XXX系，YYY大学，Location，Country）；School of ZZZ, Institute of WWW, Location, Country（ZZZ学院，WWW研究所，Location，Country）
University of Tennessee（田纳西大学）；Google Research（谷歌研究院）；Oak Ridge National Laboratory（橡树岭国家实验室）
Federated Learning (FL) enables multiple distributed clients (e.g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the client. Compared to traditional centralized machine learning, FL offers many favorable features such as offloading operations which would usually be performed by a central server and reducing risks of serious privacy leakage. However, Byzantine clients that send incorrect or disruptive updates due to system failures or adversarial attacks may disturb the joint learning process, consequently degrading the performance of t…
Federated Latent Factor Learning for Recovering Wireless Sensor Networks Signal with Privacy-Preserving
College of Computer and Information Science（计算机与信息科学学院）；Southwest University（西南大学）；School of Engineering（工程学院）；University of Western Australia（西澳大学）；College of Food Science（食品科学学院）；Chongqing True Flavor Food Co LTD（重庆真味食品有限公司）
Personalized Federated Continual Learning via Multi-granularity Prompt
School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机与人工智能学院）；Webank（微众银行）；College of Computer Science, Sichuan University（四川大学计算机学院）；JD Intelligent Cities Research（京东城市智能研究院）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）
Spectral Co-Distillation for Personalized Federated Learning
Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）；Zhejiang University/University of Illinois Urbana-Champaign Institute, Zhejiang University（浙江大学伊利诺伊大学厄巴纳-香槟校区联合学院，浙江大学）
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data
University of California, San Francisco（加州大学旧金山分校）；Lehigh University（利哈伊大学）；New Jersey Institute of Technology（新泽西理工学院）；University of Louisiana at Lafayette（路易斯安那大学拉法叶分校）
United States CCDC Army Research Laboratory（美国CCDC陆军研究实验室）
Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation (SPIM) is helpful in improving this gain by utilizing additional signal bits modulated by the indices of spatial paths. In this paper, we introduce model-based and model-free frameworks for beamformer design in multi-user SPIM-MIMO systems. We first design the beamformers via model-based manifold optimization algorithm. Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on …
Federated Independent Component Analysis via Spectral Alignment and Robust Aggregation
Department of Electrical and Computer Engineering, Rutgers University, New Brunswick（罗格斯大学电气与计算机工程系）；Department of Statistical Sciences, University of Toronto（多伦多大学统计学系）
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）
Federated Continual Learning via Knowledge Fusion: A Survey
Nanyang Technological University（南洋理工大学）；JD iCity, JD Technology（京东科技京东智城）；JD Intelligent Cities Research & Institute of Artificial Intelligence, Southwest Jiaotong University（西南交通大学京东智慧城市研究院与人工智能研究所）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）
AFCL: Analytic Federated Continual Learning for Spatio-Temporal Invariance of Non-IID Data
Peking University, China（北京大学）；South China University of Technology, China（华南理工大学）；Central South University, China（中南大学）；Beijing Normal University, China（北京师范大学）；University of Southampton, UK（南安普顿大学）；University of Maryland, Baltimore County, USA（马里兰大学巴尔的摩县分校）
