In this paper, we compare three privacy-preserving distributed learning techniques: federated learning, split learning, and SplitFed. We use these techniques to develop binary classification models for detecting tuberculosis from chest X-rays and compare them in terms of classification performance, communication and computational costs, and training time. We propose a novel distributed learning architecture called SplitFedv3, which performs better than split learning and SplitFedv2 in our experiments. We also propose alternate mini-batch training, a new training technique for split learning, that performs better than alternate client training, where clients take turns to train a model.
University of Quebec（魁北克大学）；ETS（高等技术学院）；Princeton University（普林斯顿大学）
Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centralized training approach of artificial intelligence (AI), the use of mobile and wearable IoMT devices raises privacy concerns with respect to the information that has been communicated between hospitals and end users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm has ope…
Indian Institute of Technology (Banaras Hindu University) Varanasi（瓦拉纳西印度理工学院（贝拿勒斯印度教大学））
This paper proposes Federated Learning (FL) based smart healthcare system where Medical Centers (MCs) train the local model using the data collected from patients and send the model weights to the miners in a blockchain-based robust framework without sharing raw data, keeping privacy preservation into deliberation. We formulate an optimization problem by maximizing the utility and minimizing the loss function considering energy consumption and FL process delay of MCs for learning effective models on distributed healthcare data underlying a blockchain-based framework. We propose a solution in t…
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…
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；College of Mathematics and Computer Science, Fuzhou University（福州大学数学与计算机科学学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
Mobile crowdsensing (MCS) is an emerging sensing data collection pattern with scalability, low deployment cost, and distributed characteristics. Traditional MCS systems suffer from privacy concerns and fair reward distribution. Moreover, existing privacy-preserving MCS solutions usually focus on the privacy protection of data collection rather than that of data processing. To tackle faced problems of MCS, in this paper, we integrate federated learning (FL) into MCS and propose a privacy-preserving MCS system, called \textsc{CrowdFL}. Specifically, in order to protect privacy, participants loca…
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…
Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction
School of Computer Science and Technology（计算机科学与技术学院）；Algoma University（阿尔戈马大学）；School of Electrical Engineering and Computer Science（电气工程与计算机科学学院）；University of Ottawa（渥太华大学）
Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning
School of Computing, Newcastle University（新castle大学计算机学院）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）；Institute of Health Informatics, University College London（伦敦大学学院健康信息学研究所）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（上海智能自主系统研究所，同济大学）；College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；University College London Hospital（伦敦大学学院医院）
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）
Imperial College London（帝国理工学院）；DeepMind（深度思维）；University of Oxford（牛津大学）；Case Western Reserve University（凯斯西储大学）
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user. We report early results on the Boston Housing and Pima Indian Diabetes datasets. While the privacy features apart from Differenti…
Federated learning (FL) has recently become a hot research topic, in which Byzantine robustness, communication efficiency and privacy preservation are three important aspects. However, the tension among these three aspects makes it hard to simultaneously take all of them into account. In view of this challenge, we theoretically analyze the conditions that a communication compression method should satisfy to be compatible with existing Byzantine-robust methods and privacy-preserving methods. Motivated by the analysis results, we propose a novel communication compression method called consensus …
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…
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）
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 …
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）
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）
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）
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…
L3S Research Center, Leibniz University（莱布尼茨大学L3S研究中心）；University of Applied Sciences Osnabrück（奥斯纳布吕克应用科学大学）；University of Applied Sciences and Arts Hannover（汉诺威应用科学与艺术大学）
Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, reduced cost, and it drives innovative solutions within the healthcare sector. However, health data is highly sensitive and subject to regulations such as the General Data Protection Regulation (GDPR), which aims to ensure patient's privacy. Anonymization or removal of patient identifiable information, though the most conventional way, is the first important step to adhere to the regulations and incorporate privacy concerns. In this paper, we review the existing anonymization t…
Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science（齐鲁医院山东大学神经外科和脑科学与脑启发科学研究院）；Department of Neurosurgery, The Affiliated Hospital of Qingdao University（青岛大学附属医院神经外科）；Department of Neurosurgery, Peking Union Medical College Hospital（北京地坛医院神经外科）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；College of Artificial Intelligence, Dalian Maritime University（大连海事大学人工智能学院）；University of Colorado Boulder（科罗拉多大学波尔德分校）；Zhengzhou University（郑州大学）；The University of Hong Kong（香港大学）；Georgia Institute of Technology（佐治亚理工学院）
A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers
Thumbay Institute for AI in Healthcare（坦贝医疗人工智能研究院）；Gulf Medical University（海湾医科大学）；National Sun Yat-sen University（国立中山大学）；University of Aizu（会津大学）；Indian Institute of Information Technology Bhopal（博帕尔印度信息技术学院）；University of Economics and Human Sciences（经济与人文科学大学）
An Analytical Approach to Privacy and Performance Trade-Offs in Healthcare Data Sharing
organization= Decision Sciences \& MIS Department, Drexel University , city= Philadelphia , state= PA , country= United States；organization= College of Engineering, University of Massachusetts Amherst , city= Amherst , state= MA , country= United States
Iowa State University（爱荷华州立大学）；University of South Florida（南佛罗里达大学）
For privacy concerns to be addressed adequately in current machine learning systems, the knowledge gap between the machine learning and privacy communities must be bridged. This article aims to provide an introduction to the intersection of both fields with special emphasis on the techniques used to protect the data.
This paper evaluates synthetically generated healthcare data for biases and investigates the effect of fairness mitigation techniques on utility-fairness. Privacy laws limit access to health data such as Electronic Medical Records (EMRs) to preserve patient privacy. Albeit essential, these laws hinder research reproducibility. Synthetic data is a viable solution that can enable access to data similar to real healthcare data without privacy risks. Healthcare datasets may have biases in which certain protected groups might experience worse outcomes than others. With the real data having biases, …
An Investigation of Memorization Risk in Healthcare Foundation Models
MIT Broad Institute of MIT and Harvard（MIT Broad研究所（MIT和哈佛））；Vector Institute（向量研究所）；MIT；Chalmers University of Technology（查尔姆斯理工大学）；University of Gothenburg（哥德堡大学）；University of Toronto（多伦多大学）；University Health Network (UHN)（大学健康网络）；Worcester Polytechnic Institute（沃思维尔理工学院）；Computer Science Department（计算机科学系）
University of the West England (UWE)（西英格兰大学（UWE））；Hamad Bin Khalifa University (HBKU)（哈马德·本·哈利法大学（HBKU））；Information Technology University (ITU)（信息技术大学（ITU））
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in…
Virginia Polytechnic Institute and State University (Virginia Tech)（弗吉尼亚理工学院暨州立大学（弗吉尼亚理工））
Deep learning models have demonstrated high-quality performance in areas such as image classification and speech processing. However, creating a deep learning model using electronic health record (EHR) data, requires addressing particular privacy challenges that are unique to researchers in this domain. This matter focuses attention on generating realistic synthetic data while ensuring privacy. In this paper, we propose a novel framework called correlation-capturing Generative Adversarial Network (CorGAN), to generate synthetic healthcare records. In CorGAN we utilize Convolutional Neural Netw…
HealthSLM-Bench: Benchmarking Small Language Models for Mobile and Wearable Healthcare Monitoring
School of Computing and Information Systems, University of Melbourne, Australia（墨尔本大学计算机与信息系统学院）；School of Computer Science, University of Auckland, New Zealand（奥克兰大学计算机科学学院）
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）；Microsoft Research Asia（微软亚洲研究院）
The success of machine learning applications often needs a large quantity of data. Recently, federated learning (FL) is attracting increasing attention due to the demand for data privacy and security, especially in the medical field. However, the performance of existing FL approaches often deteriorates when there exist domain shifts among clients, and few previous works focus on personalization in healthcare. In this article, we propose FedHealth 2, an extension of FedHealth \cite{chen2020fedhealth} to tackle domain shifts and get personalized models for local clients. FedHealth 2 obtains the …
PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding
Hubei Key Laboratory of Brain-inspired Intelligent Systems, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院脑启发智能系统湖北省重点实验室）；Shenzhen Huazhong University of Science and Technology Research Institute（深圳华中科技大学研究院）；Zhongguancun Academy（中关村学院）
Hasselt University（哈瑟尔特大学）；Transnational University Limburg（林堡跨国大学）；Eindhoven University of Technology（埃因霍温理工大学）
We consider privacy preserving decision tree induction via ID3 in the case where the training data is horizontally or vertically distributed. Furthermore, we consider the same problem in the case where the data is both horizontally and vertically distributed, a situation we refer to as grid partitioned data. We give an algorithm for privacy preserving ID3 over horizontally partitioned data involving more than two parties. For grid partitioned data, we discuss two different evaluation methods for preserving privacy ID3, namely, first merging horizontally and developing vertically or first mergi…
Fraunhofer HHI（弗劳恩霍夫HHI研究所）；Photonic Networks and Systems（光子网络与系统）
We present a privacy-preserving distributed learning framework for telecom ecosystems in the 6G-era that enables the vision of shared ownership and governance of ML models, while protecting the privacy of the data owners. We demonstrate its benefits by applying it to the use-case of Quality of Transmission (QoT) estimation in multi-domain multi-vendor optical networks, where no data of individual domains is shared with the network management system (NMS).
