Federated Learning (FL) is a distributed learning scheme that enables deep learning to be applied to sensitive data streams and applications in a privacy-preserving manner. This paper focuses on the use of FL for analyzing smart energy meter data with the aim to achieve comparable accuracy to state-of-the-art methods for load forecasting while ensuring the privacy of individual meter data. We show that with a lightweight fully connected deep neural network, we are able to achieve forecasting accuracy comparable to existing schemes, both at each meter source and at the aggregator, by utilising the FL framework. The use of lightweight models further reduces the energy and resource consumption caused by complex deep-learning models, making this approach ideally suited for deployment across resource-constrained smart meter systems. With our proposed lightweight model, we are able to achieve an overall average load forecasting RMSE of 0.17, with the model having a negligible energy overhead of 50 mWh when performing training and inference on an Arduino Uno platform.
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）
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）
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）
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）
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）
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）
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）
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））
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）
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））
REFOL: Resource-Efficient Federated Online 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）；Intelligent Transportation Thrust, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）智能交通枢纽）；School of Software, Nanjing University of Information Science and Technology（南京信息工程大学软件学院）；Zhongguancun Lab（中关村实验室）；School of Computing and Communications, Lancaster University（兰卡斯特大学计算与通信学院）；JD Intelligent Cities Research（京东智能城市研究院）；JD iCity, JD Technology（京东科技京东智城）
Federated Learning for Water Consumption Forecasting in Smart Cities
Mohammed V University in Rabat（拉巴特穆罕默德五世大学）；ENSIAS（国家高等信息与系统分析学院（拉巴特穆罕默德五世大学下属，按独立机构提取为ENSIAS，中文暂译：国家高等信息与系统分析学院））；Al Akhawayn University in Ifrane（伊夫兰阿卡韦恩大学）；School of Science and Engineering（科学与工程学院）
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning
Technische Universität Berlin（柏林工业大学）；Sharif University of Technology（谢里夫理工大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
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）
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting
AMSS Center for Forecasting Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院预测科学中心）；MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation, University of Chinese Academy of Sciences（中国科学院大学数字经济预测与政策模拟教育部社会科学实验室）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
针对高阶多项式负荷预测面临的维度灾难、过拟合和算力限制，本文提出结合低秩近似、自监督降维与快速共轭梯度算法的 HOPS，并在 ISO New England 数据集上验证其精度与变量精简优势。
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
University of New South Wales（新南威尔士大学）；CSIRO（联邦科学与工业研究组织）
