Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids
Department of Computational and Data Sciences, Indian Institute of Science (IISc), Bangalore, India（计算与数据科学系，印度科学研究院（IISc），班加罗尔，印度）；Department of Electrical Engineering, Indian Institute of Science (IISc), Bangalore, India（电气工程系，印度科学研究院（IISc），班加罗尔，印度）；Accenture, India（印度德勤）
Real-time monitoring of power consumption in cities and micro-grids through the Internet of Things (IoT) can help forecast future demand and optimize grid operations. But moving all consumer-level usage data to the cloud for predictions and analysis at fine time scales can expose activity patterns. Federated Learning~(FL) is a privacy-sensitive collaborative DNN training approach that retains data on edge devices, trains the models on private data locally, and aggregates the local models in the cloud. But key challenges exist: (i) clients can have non-independently identically distributed~(non-IID) data, and (ii) the learning should be computationally cheap while scaling to 1000s of (unseen) clients. In this paper, we develop and evaluate several optimizations to FL training across edge and cloud for time-series demand forecasting in micro-grids and city-scale utilities using DNNs to achieve a high prediction accuracy while minimizing the training cost. We showcase the benefit of using exponentially weighted loss while training and show that it further improves the prediction of the final model. Finally, we evaluate these strategies by validating over 1000s of clients for three states in the US from the OpenEIA corpus, and performing FL both in a pseudo-distributed setting and a Pi edge cluster. The results highlight the benefits of the proposed methods over baselines like ARIMA and DNNs trained for individual consumers, which are not scalable.
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）
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 fram…
University of Alberta（阿尔伯塔大学）；University of Hradec Králové（赫拉德茨-克拉洛韦大学）
Electrical load prediction has become an integral part of power system operation. Deep learning models have found popularity for this purpose. However, to achieve a desired prediction accuracy, they require huge amounts of data for training. Sharing electricity consumption data of individual households for load prediction may compromise user privacy and can be expensive in terms of communication resources. Therefore, edge computing methods, such as federated learning, are gaining more importance for this purpose. These methods can take advantage of the data without centrally storing it. This p…
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）
As Smart Meters are collecting and transmitting household energy consumption data to Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine-grained consumer data while ensuring data privacy. In this manuscript, we tackle this challenge for energy load consumption forecasting in regards to REPs which is essential to energy demand management, load switching and infrastructure development. Specifically, we note that existing energy load forecasting is centralized, which are not scalable and most importantly, vulnerable to data privacy threats. Besides, REPs are i…
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）
Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising privacy-preserving machine learning alternative which enables collaborative learning of a model without exposing private raw data for short term load forecasting. Despite its virtue, standard FL is still vulnerable to an intractable cyber threat known as Byzantine attack carried out by faulty and/or malicious clients. Therefore, to improve the robustness of federat…
FINATRAX, University of Luxembourg（卢森堡大学FINATRAX）；University of Luxembourg（卢森堡大学）
With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential l…
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）
Northeast Electric Power University（东北电力大学）；State Grid Jilin Electric Power Company Limited（国网吉林省电力有限公司）
Load forecasting is very essential in the analysis and grid planning of power systems. For this reason, we first propose a household load forecasting method based on federated deep learning and non-intrusive load monitoring (NILM). For all we know, this is the first research on federated learning (FL) in household load forecasting based on NILM. In this method, the integrated power is decomposed into individual device power by non-intrusive load monitoring, and the power of individual appliances is predicted separately using a federated deep learning model. Finally, the predicted power values …
Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transitions towards less reliable renewable energy generation, smart meters will prove a vital component to facilitate these forecasting tasks. However, smart meter adoption is low among privacy-conscious consumers that fear intrusion upon their fine-grained consumption data. In this work we propose and explore a federated learning (FL) based approach for training forecasting models in a distributed, collaborative manner whi…
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）
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）
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）
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）
Water consumption remains a major concern among the world's future challenges. For applications like load monitoring and demand response, deep learning models are trained using enormous volumes of consumption data in smart cities. On the one hand, the information used is private. For instance, the precise information gathered by a smart meter that is a part of the system's IoT architecture at a consumer's residence may give details about the appliances and, consequently, the consumer's behavior at home. On the other hand, enormous data volumes with sufficient variation are needed for the deep …
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 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）
Machine learning (ML) based smart meter data analytics is very promising for energy management and demand-response applications in the advanced metering infrastructure(AMI). A key challenge in developing distributed ML applications for AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes a privacy-preserving federated learning framework for ML applications in the AMI. We consider each smart meter as a federated edge device hosting an ML application that exchanges information with a central aggregator or a data concentra…
In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer's household such as occupancy, habits and individual appliance usage. Yet smart metering infrastructure has the potential to vastly reduce carbon emissions from the energy sector through improved operating efficiencies. We propose the application of a distributed machine learning setting known as federated learning for energy demand forecasting at vari…
With increasing penetration of Distributed Energy Resources (DERs) in grid edge including renewable generation, flexible loads, and storage, accurate prediction of distributed generation and consumption at the consumer level becomes important. However, DER prediction based on the transmission of customer level data, either repeatedly or in large amounts, is not feasible due to privacy concerns. In this paper, a distributed machine learning approach, Federated Learning, is proposed to carry out DER forecasting using a network of IoT nodes, each of which transmits a model of the consumption and …
Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing fine-grained energy data and performing centralised learning may compromise users' privacy and leave them vulnerable to several attacks. This study…
