With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets.
Sun Yat-sen University（中山大学）；Arizona State University（亚利桑那州立大学）
In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and recent development in machine learning, smart healthcare has seen many successful stories. However, existing approaches for in-home health monitoring do not pay sufficient attention to user data privacy and thus are far from being ready for large-scale practical deployment. In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global…
Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare indust…
Rice University（莱斯大学）；University of Houston-Downtown（休斯顿大学市中心分校）；Prudence Medical Technologies Ltd.（审慎医疗技术有限公司）
The Internet of Things (IoT) revolution has shown potential to give rise to many medical applications with access to large volumes of healthcare data collected by IoT devices. However, the increasing demand for healthcare data privacy and security makes each IoT device an isolated island of data. Further, the limited computation and communication capacity of wearable healthcare devices restrict the application of vanilla federated learning. To this end, we propose an advanced federated learning framework to train deep neural networks, where the network is partitioned and allocated to IoT devic…
Australian Department of Health（澳大利亚卫生部）；University of Technology Sydney（悉尼科技大学）
Privacy protection is an ethical issue with broad concern in Artificial Intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across users or organisations without direct access to the data. It has great potential to be the next-general AI model training framework that offers privacy protection and therefore has broad implications for the future of digital health and healthcare informatics. Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organi…
University of Essex（埃塞克斯大学）；Durham University（杜伦大学）；University College Cork（科克大学学院）
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumptions, can help to analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications. However, smart meters are privately owned and distributed, which make real-world applications of NILM challenging. To this end, this paper develops a distributed and privacy-preserving federated deep learning framework for NILM (FederatedNILM), which combines federated l…
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）
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare
Indian Institute of Technology, Bombay（印度理工学院孟买分校）；Sardar Patel Institute of Technology（萨达尔·帕特尔理工学院）；Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）
Federated learning opens a number of research opportunities due to its high communication efficiency in distributed training problems within a star network. In this paper, we focus on improving the communication efficiency for fully decentralized federated learning over a graph, where the algorithm performs local updates for several iterations and then enables communications among the nodes. In such a way, the communication rounds of exchanging the common interest of parameters can be saved significantly without loss of optimality of the solutions. Multiple numerical simulations based on large…
Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning
University of Cincinnati（辛辛那提大学）；University of Wisconsin Milwaukee（威斯康星大学密尔沃基分校）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）；University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；Northeastern University（东北大学）
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Information Technology University (ITU) Punjab（旁遮普信息技术大学（ITU））
Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, have gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by analyzing and evaluating the potential of intelligent processing of clinical visual data at the …
Weill Cornell Medicine（威尔康奈尔医学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；U.S. Department of Defense Joint Artificial Intelligence Center（美国国防部联合人工智能中心）；University of Florida（佛罗里达大学）
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of diff…
Federated learning (FL) aided health diagnostic models can incorporate data from a large number of personal edge devices (e.g., mobile phones) while keeping the data local to the originating devices, largely ensuring privacy. However, such a cross-device FL approach for health diagnostics still imposes many challenges due to both local data imbalance (as extreme as local data consists of a single disease class) and global data imbalance (the disease prevalence is generally low in a population). Since the federated server has no access to data distribution information, it is not trivial to solv…
Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained efficiently without moving health data out of their silos using a distributed machine learning strategy. We propose a new method, called Federated-Autonomous Deep Learning (FADL) that trains part of the model using all data sources in a dist…
ConsenSys Health；Imperial College London（帝国理工学院）；UC San Diego（加州大学圣地亚哥分校）；Johns Hopkins Berman Institute of Bioethics（约翰斯·霍普金斯大学伯曼生物伦理研究所）
We propose a novel architecture for federated learning within healthcare consortia. At the heart of the solution is a unique integration of privacy preserving technologies, built upon native enterprise blockchain components available in the Ethereum ecosystem. We show how the specific characteristics and challenges of healthcare consortia informed our design choices, notably the conception of a new Secure Aggregation protocol assembled with a protected hardware component and an encryption toolkit native to Ethereum. Our architecture also brings in a privacy preserving audit trail that logs eve…
Non-intrusive load monitoring (NILM) is essential for understanding customer's power consumption patterns and may find wide applications like carbon emission reduction and energy conservation. The training of NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners during the NILM model training. To prevent such potential risks, a novel NILM method named Fed-NILM which is based on Federated Learning (FL) is proposed in this paper. In Fed-NILM, local mode…
Healthcare IoMT systems are becoming intelligent, miniaturized, and more integrated into daily life. As for the distributed devices in the IoMT, federated learning has become a topical area with cloud-based training procedures when meeting data security. However, the distribution of IoMT has the risk of protection from data poisoning attacks. Poisoned data can be fabricated by falsifying medical data, which urges a security defense to IoMT systems. Due to the lack of specific labels, the filtering of malicious data is a unique unsupervised scenario. One of the main challenges is finding robust…
Royal Military College of Canada（加拿大皇家军事学院）；Universidad Nacional Autónoma de México（墨西哥国立自治大学）
Federated Learning (FL) is a machine learning technique that addresses the privacy challenges in terms of access rights of local datasets by enabling the training of a model across nodes holding their data samples locally. To achieve decentralized federated learning, blockchain-based FL was proposed as a distributed FL architecture. In decentralized FL, the chief is eliminated from the learning process as workers collaborate between each other to train the global model. Decentralized FL applications need to account for the additional delay incurred by blockchain-based FL deployments. Particula…
Multimodal Federated Learning in Healthcare: a Review
West Virginia University（西弗吉尼亚大学）；Tribhuvan University（特里布万大学）；University at Albany（奥尔巴尼大学）；University of Pittsburgh（匹兹堡大学）；University of Aberdeen（阿伯丁大学）
IBM Research Cambridge（IBM剑桥研究院）；IBM Watson（IBM沃森）；IBM T.J. Watson Research Center（IBM T.J.沃森研究中心）；Massachusetts Institute of Technology（麻省理工学院）
Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraints for transferring and integrating data from multiple sites, and risk of a single point of failure. In this paper, we introduce a federated learning framework that can learn a global model from distributed health data held locally at different sites. The framework offers two levels of privacy protection. First, it does not move or share raw data ac…
Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare
Purdue University（普渡大学）；Davidson School of Chemical Engineering（戴维森化学工程学院）；Research Institute for Advanced Computer Science（高级计算机科学研究所）；Universities Space Research Association（大学空间研究协会）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）
Global AI Accelerator, Ericsson（爱立信全球人工智能加速器）；Ericsson Research（爱立信研究院）
Various health-care applications such as assisted living, fall detection, etc., require modeling of user behavior through Human Activity Recognition (HAR). Such applications demand characterization of insights from multiple resource-constrained user devices using machine learning techniques for effective personalized activity monitoring. On-device Federated Learning proves to be an effective approach for distributed and collaborative machine learning. However, there are a variety of challenges in addressing statistical (non-IID data) and model heterogeneities across users. In addition, in this…
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare
Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科学与技术大学）；Department of Mathematical Sciences, University of Alabama in Huntsville（数学科学系，阿拉巴马大学亨茨维尔分校）
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness
Guangdong University of Technology（广东工业大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Nanyang Technological University（南洋理工大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Royal Military College of Canada（加拿大皇家军事学院）；Ryerson University（瑞尔森大学）
In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief. Regarding the cybersecurity of FL, some attacks aim at injecting the fabricated local model updates into the system. Some defenses are based on malicious worker detection and behavioral pattern analysis. In this context, without timely and dynamic monitoring methods, the chief cannot detect and remove the malicious or unreliable workers from the system. Our work emphasize the urgency to prepare the federated learning process for monitoring and eventually behavioral …
University of Guelph（圭尔夫大学）；University of Toronto（多伦多大学）
Over the years, the popularity and usage of wearable Internet of Things (IoT) devices in several healthcare services are increased. Among the services that benefit from the usage of such devices is predictive analysis, which can improve early diagnosis in e-health. However, due to the limitations of wearable IoT devices, challenges in data privacy, service integrity, and network structure adaptability arose. To address these concerns, we propose a platform using federated learning and private blockchain technology within a fog-IoT network. These technologies have privacy-preserving features se…
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）
Deakin University（迪肯大学）；Pusan National University（釜山国立大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；University of New South Wales（新南威尔士大学）；The University of Sydney（悉尼大学）；Memorial University（纪念大学）
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing …
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 …
Recent Methodological Advances in Federated Learning for Healthcare
University of Cambridge（剑桥大学）；University of Bremen（不来梅大学）；University of Helsinki（赫尔辛基大学）；Addenbrooke’s Hospital, Cambridge University Hospitals NHS Trust（剑桥大学医院NHS信托基金会阿登布鲁克医院）
A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction
Singapore University of Technology and Design（新加坡科技设计大学）；Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）前沿人工智能研究中心）；Chongqing University（重庆大学）；Nanyang Technological University（南洋理工大学）
University of Bristol（布里斯托大学）；Toshiba Europe Ltd.（东芝欧洲有限公司）
Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications. In the context of an Internet of Things (IoT) ecosystem, the data communication is frequently costly, inefficient, not scalable and lacks security. Federated Learning (FL) plays a pivotal role in providing privacy-preserving and communication efficient Machine Learning (ML) frameworks. In this paper we evaluate the feasibility of FL in the context of a Smart Cities Street Light Monitoring application. FL is evaluated against benchmarks of centralised and (fully) personalise…
National College of Business Administration and Economics（国家工商经济学院）；Riphah International University（里法哈国际大学）；Skyline University College（天际线大学学院）；Universiti Kebangsaan Malaysia（马来西亚国民大学）；Gachon University（加川大学）；Slovak University of Technology in Bratislava（布拉迪斯拉发斯洛伐克理工大学）；Obuda University（欧布达大学）；TU-Dresden（德累斯顿工业大学）
In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT ena…
The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository. This process necessitates communication of model weights or updates between collaborating entities, but it is unclear to what extent patient privacy is compromised as a result. To gain insight into this question, we study the efficacy of centralized versus federated learning in both private and non-pri…
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Non-intrusive load monitoring (NILM) aims at decomposing the total reading of the household power consumption into appliance-wise ones, which is beneficial for consumer behavior analysis as well as energy conservation. NILM based on deep learning has been a focus of research. To train a better neural network, it is necessary for the network to be fed with massive data containing various appliances and reflecting consumer behavior habits. Therefore, data cooperation among utilities and DNOs (distributed network operators) who own the NILM data has been increasingly significant. During the coope…
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Microsoft Research Asia（微软亚洲研究院）；Pengcheng Laboratory（鹏城实验室）；Zhejiang University（浙江大学）；Microsoft Research（微软研究院）
There is a growing interest in applying machine learning techniques to healthcare. Recently, federated learning (FL) is gaining popularity since it allows researchers to train powerful models without compromising data privacy and security. However, the performance of existing FL approaches often deteriorates when encountering non-iid situations where there exist distribution gaps among clients, and few previous efforts focus on personalization in healthcare. In this article, we propose FedAP to tackle domain shifts and then obtain personalized models for local clients. FedAP learns the similar…
Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences
STADIUS, Department of Electrical Engineering (ESAT), KU Leuven（STADIUS部门，电气工程系（ESAT），鲁汶大学）；Biomedical Research Institute (BIOMED), Hasselt University（生物医学研究所（BIOMED），哈塞尔特大学）；Data Science Institute (DSI), Hasselt University（数据科学研究所（DSI），哈塞尔特大学）；Department of Medical Genetics, University of Antwerp（医学遗传学系，安特卫普大学）
Wind turbine condition monitoring based on intra- and inter-farm federated learning
Bern University of Applied Sciences, School of Engineering（伯恩应用科学大学工程学院）；Università della Svizzera italiana, Faculty of Informatics（瑞士联合大学信息学院）；Delft University of Technology, Department of Geoscience（代尔夫特理工大学地质学院）；Remote Sensing, Stevinweg 1, 2628 Delft, The Netherlands（遥感学院，Stevinweg 1，2628 Delft，荷兰）
