Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
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）；Zhongguancun Laboratory（中关村实验室）；School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）
Qingxiang Liu, Sheng Sun, Min Liu, Yuwei Wang, Bo Gao
Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems (ITS). To mitigate communication burden and tackle with the problem of privacy leakage aroused by centralized forecasting methods, Federated Learning (FL) has been applied to TFF. However, existing FL-based approaches employ batch learning manner, which makes the pre-trained models inapplicable to subsequent traffic data, thus exhibiting subpar prediction performance. In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL framework and then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance gains regardless of traffic fluctuation. Specifically, clients employ Gated Recurrent Unit (GRU)-based encoders to obtain the internal temporal patterns inside traffic data sequences. Then, the central server evaluates spatial correlation among clients via Graph Attention Network (GAT), catering to the dynamic changes of spatial closeness caused by traffic fluctuation. Furthermore, to improve the generalization of the global model for upcoming traffic data, a period-aware aggregation mechanism is proposed to aggregate the local models which are optimized using Online Gradient Descent (OGD) algorithm at clients. We perform comprehensive experiments on two real-world datasets to validate the efficiency and effectiveness of our proposed method and the numerical results demonstrate the superiority of FedOSTC.
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning
Department of Electrical and Computer Engineering, University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校电气与计算机工程系）；Computer Science and Mathematics Division, Oak Ridge National Laboratory（橡树岭国家实验室计算机科学与数学 division）；Department of Electrical and Computer Engineering, Kansas State University（堪萨斯州立大学电气与计算机工程系）
Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learning
DISMI, University of Modena and Reggio Emilia, Italy（DISMI，Modena和Reggio Emilia大学）；En\&Tech, University of Modena and Reggio Emilia, Italy（En\&Tech，Modena和Reggio Emilia大学）
Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shanghai Jiao Tong University（上海交通大学）；Harbin Engineering University（哈尔滨工程大学）
Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models
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）；Department of Computer Science, Bahria University（巴里亚大学计算机科学系）；School of Mechanical Engineering, Southwest Jiaotong University（西南交通大学机械工程学院）
On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning
Department of Informatics, University of Piraeus（信息学院，比雷埃克斯大学）；Center for Power and Energy Systems, INESC TEC（能源系统中心，INESC TEC）；Department of Statistics and Insurance Science, University of Piraeus（统计与保险科学系，比雷埃克斯大学）
Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization
School of Electrical Engineering and Computer Science, The Pennsylvania State University（宾夕法尼亚州立大学电气工程与计算机科学学院）；Department of Electrical and Computer Engineering, Bucknell University（巴克内尔大学电气与计算机工程系）
MoE Enhanced Federated Learning for Spatiotemporal Prediction
Zhejiang University of Technology, Zhejiang Key Laboratory of Visual Information Intelligent Processing（浙江工业大学，浙江省可视信息智能处理重点实验室）；Jilin University（吉林大学）；City University of Hong Kong（香港城市大学）
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））
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting
COSYS-GRETTIA, Gustave Eiffel University, 77420 France（COSYS-GRETTIA，巴黎-伊夫林大学）；ENTPE, University of Lyon（ENTPE，里昂大学）；the LICIT-ECO7 University Gustave Eiffel, France（LICIT-ECO7 巴黎-伊夫林大学）
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）；Baidu Research, Baidu Inc.（百度研究院，百度公司）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；BOSS Zhipin（BOSS直聘）
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting
Democritus University of Thrace（德谟克利特色雷斯大学）；Imperial College London（帝国理工学院）；Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA)（加泰罗尼亚电信技术中心（CTTC/CERCA））；Nokia Bell Labs（诺基亚贝尔实验室）；IMT Atlantique（大西洋电信学校）；Inria（法国国家信息与自动化研究所）；LS2N（南特计算机科学与实验室（注：此处LS2N为实验室，按要求保留独立机构名））
Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting
DISMI, University of Modena and Reggio Emilia（DISMI，Modena和Reggio Emilia大学）；En\&Tech, University of Modena and Reggio Emilia（En\&Tech，Modena和Reggio Emilia大学）；DIN, University of Bologna（DIN，博洛尼亚大学）
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach
Northeast Electric Power University（东北电力大学）；State Grid Jilin Electric Power Co., Ltd. Jilin Power Supply Company（国网吉林省电力有限公司吉林供电公司）；Huazhong University of Science and Technology（华中科技大学）；Naval University of Engineering（海军工程大学）；Cranfield University（克兰菲尔德大学）
Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model
School of Engineering and Technology, China University of Geosciences (Beijing)（工程与技术学院，中国地质大学（北京））；School of Geological Engineering and Geomatics, Chang'an University（地质工程与测绘学院，长安大学）
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；National University of Singapore（新加坡国立大学）；Salesforce AI Research（Salesforce AI研究部）；Squirrel AI（松鼠AI）
交通时间序列预测是具有挑战性的，因为来自不同位置的复杂时空动态时间序列通常具有不同的模式;并且对于相同的时间序列，模式可能随时间而变化，例如，在一天中存在显示出更强的时间相关性的某些时段。尽管最近的预测模型，特别是基于深度学习的模型，显示出有希望的结果，但它们受到时空不可知论的影响。这样的时空不可知模型采用共享参数空间，而不管时间序列位置和时间段，并且它们假设时间模式在位置之间是相似的，并且不会在时间上演变，这可能并不总是成立，从而导致次优结果。在这项工作中，我们提出了一个框架，旨在把时空不可知模型时空感知模型。为此，我们将来自不同位置的时间序列编码为随机变量，从中生成特定于位置和随时间变化的模型参数，以更好地捕捉时空动态。我们展示了如何整合的框架与规范的注意，使时空意识的注意。其次，为了补偿时空感知模型参数生成过程中引入的额外开销，提出了一种新的窗口注意机制，将复杂度从二次降低到线性，使得时空感知注意也具有竞争效率.我们在四个交通时间序列数据集上展示了强有力的经验证据，其中所提出的时空感知注意力在准确性和效率方面优于最先进的方法。这是“Towards Spatio-Temporal Aware Traffic Time Series Forecasting”的扩展版本，将出现在ICDE 2022 [1]中，包括其他实验结果。
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）；The Hong Kong University of Science and Technology（香港科技大学）
Tackling Data Heterogeneity in Federated Time Series Forecasting
The University of Queensland（昆士兰大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；City University of Hong Kong（香港城市大学）；Griffith University（格里菲斯大学）；Institute of Science Tokyo（东京科学大学）
