Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, real-world applications involve survival datasets that are distributed, incomplete, censored, and confidential. In this context, federated learning can tremendously improve the performance of survival analysis applications. Federated learning provides a set of privacy-preserving techniques to jointly train machine learning models on multiple datasets without compromising user privacy, leading to a better generalization performance. However, despite the widespread development of federated learning in recent AI research, few studies focus on federated survival analysis. In this work, we present a novel federated algorithm for survival analysis based on one of the most successful survival models, the random survival forest. We call the proposed method Federated Survival Forest (FedSurF). With a single communication round, FedSurF obtains a discriminative power comparable to deep-learning-based federated models trained over hundreds of federated iterations. Moreover, FedSurF retains all the advantages of random forests, namely low computational cost and natural handling of missing values and incomplete datasets. These advantages are especially desirable in real-world federated environments with multiple small datasets stored on devices with low computational capabilities. Numerical experiments compare FedSurF with state-of-the-art survival models in federated networks, showing how FedSurF outperforms deep-learning-based federated algorithms in realistic environments with non-identically distributed data.
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）
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
Survival analysis, time-to-event analysis, is an important problem in healthcare since it has a wide-ranging impact on patients and palliative care. Many survival analysis methods have assumed that the survival data is centrally available either from one medical center or by data sharing from multi-centers. However, the sensitivity of the patient attributes and the strict privacy laws have increasingly forbidden sharing of healthcare data. To address this challenge, the research community has looked at the solution of decentralized training and sharing of model parameters using the Federated L…
A learning federation is composed of multiple participants who use the federated learning technique to collaboratively train a machine learning model without directly revealing the local data. Nevertheless, the existing federated learning frameworks have a serious defect that even a participant is revoked, its data are still remembered by the trained model. In a company-level cooperation, allowing the remaining companies to use a trained model that contains the memories from a revoked company is obviously unacceptable, because it can lead to a big conflict of interest. Therefore, we emphatical…
Principled Federated Random Forests for Heterogeneous Data
Inria（法国国家信息与自动化研究所）；PreMeDICaL（预医学实验室）；Inserm（法国国家医学研究院）；University of Montpellier（蒙彼利埃大学）；Sorbonne Université（索邦大学）；Université Paris Cité（巴黎城市大学）；CNRS（国家科学研究中心）；LPSM（巴黎高等师范学院）
Owkin Inc.（Owkin公司）；Hospital Israelita Albert Einstein（爱因斯坦以色列医院）；French Red Cross Innovation（法国红十字会创新中心）
Building machine learning models from decentralized datasets located in different centers with federated learning (FL) is a promising approach to circumvent local data scarcity while preserving privacy. However, the prominent Cox proportional hazards (PH) model, used for survival analysis, does not fit the FL framework, as its loss function is non-separable with respect to the samples. The naïve method to bypass this non-separability consists in calculating the losses per center, and minimizing their sum as an approximation of the true loss. We show that the resulting model may suffer from imp…
As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed to preserve the data privacy of the participating organizations. However, the efficiency of existing vertically federated learning algorithms remains to be a big problem, especially when applied to large-scale real-world datasets. In this paper, we present a fast, accurate, scalable and yet robust system for vertically federated random forest. With extensive optimization, we achieved $5\times$ and $83\times$ speed u…
CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；DKFZ German Cancer Research Center（德国癌症研究中心）
Survival analysis or time-to-event analysis aims to model and predict the time it takes for an event of interest to happen in a population or an individual. In the medical context this event might be the time of dying, metastasis, recurrence of cancer, etc. Recently, the use of neural networks that are specifically designed for survival analysis has become more popular and an attractive alternative to more traditional methods. In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of these models. This is crucial in the medical domain…
Privacy-preserving federated prediction of pain intensity change based on multi-center survey data
University of Hamburg（汉堡大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；University of Southern Denmark（南丹麦大学）；Næstved-Slagelse-Ringsted Hospitals（奈斯特韦兹-斯拉厄尔瑟-灵斯泰兹医院）
Beihang University（北京航空航天大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Data protection across organizations is limiting the application of centralized learning (CL) techniques. Federated learning (FL) enables multiple participants to build a learning model without sharing data. Nevertheless, there are very few research works on FL in intelligent manufacturing. This paper presents the results of an empirical study on failure prediction in the production line based on FL. This paper (1) designs Federated Support Vector Machine (FedSVM) and Federated Random Forest (FedRF) algorithms for the horizontal FL and vertical FL scenarios, respectively; (2) proposes an exper…
Yale University（耶鲁大学）；Renmin University of China（中国人民大学）；JD Technology Group（京东科技集团）
Federated learning, conducive to solving data privacy and security problems, has attracted increasing attention recently. However, the existing federated boosting model sequentially builds a decision tree model with the weak base learner, resulting in redundant boosting steps and high interactive communication costs. In contrast, the federated bagging model saves time by building multi-decision trees in parallel, but it suffers from performance loss. With the aim of obtaining an outstanding performance with less time cost, we propose a novel model in a vertically federated setting termed as Fe…
FedMultiEmo: Real-Time Emotion Recognition via Multimodal Federated Learning
Institute of Industrial Automation and Software Engineering, University of Stuttgart, Germany（工业自动化与软件工程研究所，斯图加特大学）；Electrical Engineering, University of Stuttgart, Germany（电气工程，斯图加特大学）；Graduate School of Excellence advanced Manufacturing Engineering, University of Stuttgart, Germany（先进制造工程优秀研究生院，斯图加特大学）
Politecnico di Milano（米兰理工大学）；Technische Universität Dresden（德累斯顿工业大学）
Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, the data needed to train survival models are often distributed, incomplete, censored, and confidential. In this context, federated learning can be exploited to tremendously improve the quality of the models trained on distributed data while preserving user privacy. However, federated survival analysis is still in its early development, and there is no common benchmarking dataset to test …
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning
Institute for Neuroinformatics, Ruhr University Bochum（波鸿鲁尔大学神经信息学研究所）；Monash University（莫纳什大学）；Case Western Reserve University（凯斯西储大学）；Carenostics（Carenostics公司）
LF2L: Loss Fusion Horizontal Federated Learning Across Heterogeneous Feature Spaces Using External Datasets Effectively: A Case Study in Second Primary Cancer Prediction
National Yang Ming Chiao Tung University（国立阳明交通大学）
GlaxoSmithKline（葛兰素史克）；ETH Zürich（苏黎世联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Kantonsspital Winterthur（温特图尔州立医院）；KTH（瑞典皇家理工学院）
Federated learning (FL) has been proposed as a method to train a model on different units without exchanging data. This offers great opportunities in the healthcare sector, where large datasets are available but cannot be shared to ensure patient privacy. We systematically investigate the effectiveness of FL on the publicly available eICU dataset for predicting the survival of each ICU stay. We employ Federated Averaging as the main practical algorithm for FL and show how its performance changes by altering three key hyper-parameters, taking into account that clients can significantly vary in …
OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework
Meituan（美团）；Bytedance Inc.（字节跳动公司）；Tsinghua University（清华大学）；University of Macau（澳门大学）；State Key Lab of Computer Science, ISCAS（中国科学院软件研究所计算机科学国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Peter the Great St.Petersburg Polytechnic University (SPbPU)（圣彼得堡彼得大帝理工大学）；St.Petersburg Clinical Research Center for Special Types of Medical Care (Oncology-oriented)（圣彼得堡特殊医疗类型临床研究中心（肿瘤方向））
A weighted random survival forest is presented in the paper. It can be regarded as a modification of the random forest improving its performance. The main idea underlying the proposed model is to replace the standard procedure of averaging used for estimation of the random survival forest hazard function by weighted avaraging where the weights are assigned to every tree and can be veiwed as training paremeters which are computed in an optimal way by solving a standard quadratic optimization problem maximizing Harrell's C-index. Numerical examples with real data illustrate the outperformance of…
FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data
Institute for Predictive Deep Learning in Medicine and Healthcare（预测医学与健康人工智能研究所）；Justus Liebig University Gießen（吉森约瑟夫·李比希大学）；Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心 (hessian.AI)）；Department of Medical Informatics（医学信息学系）；University Medical Center Göttingen（哥廷根大学医学中心）；Max Planck Institute for Biology of Ageing（马克斯·普朗克衰老生物学研究所）
National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）
Federated learning (FL) is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data to each other. This paper studies {\it vertical} federated learning, which tackles the scenarios where (i) collaborating organizations own data of the same set of users but with disjoint features, and (ii) only one organization holds the labels. We propose Pivot, a novel solution for privacy preserving vertical decision tree training and prediction, ensuring that no intermediate information is disclosed other than those the clients have agreed to rel…
Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other. Recently, vertical FL, where the participating organizations hold the same set of samples but with disjoint features and only one organization owns the labels, has received increased attention. This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL. The attack methods consider the most stringent setting that the adversary controls only the t…
MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning
Maastricht University Medical Centre +（马斯特里赫特大学医学中心）；Leiden University Medical Center（莱顿大学医学中心）；Delft University of Technology（代尔夫特理工大学）；Erasmus MC - University Medical Center Rotterdam（伊拉斯姆斯大学医学中心鹿特丹分校）；Leiden University（莱顿大学）
The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistics, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to…
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in a survival and observational setting where outcomes may be right-censored. Our approach relies on orthogonal estimating equations to robustly adjust for both censoring and selection effects under unconfoundedness. In our experiments, we find our approach to perform well relative to a number of baselines.
In this paper, we present Fedlearn-Algo, an open-source privacy preserving machine learning platform. We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms. As the first batch of novel FL algorithm examples, we release vertical federated kernel binary classification model and vertical federated random forest model. They have been tested to be more efficient than existing vertical federated learning models in our practice. Besides the novel FL algorithm examples, we also release a machine communication module. The uniform data…
Texas A&M University（德克萨斯农工大学）；Department of Industrial and Systems Engineering（工业与系统工程系）；Heart, Artery, and Vein Center of Fresno（弗雷斯诺心脏、动脉和静脉中心）
Accuracies of survival models for life expectancy prediction as well as critical-care applications are significantly compromised due to the sparsity of samples and extreme imbalance between the survival (usually, the majority) and mortality class sizes. While a recent random survival forest (RSF) model overcomes the limitations of the proportional hazard assumption, an imbalance in the data results in an underestimation (overestimation) of the hazard of the mortality (survival) classes. A balanced random survival forests (BRSF) model, based on training the RSF model with data generated from a …
A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs
Chengdu Medical College（成都医学院）；Kunming Medical University（昆明医科大学）；National Yang Ming Chiao Tung University（国立阳明交通大学）；University of Southern California（南加州大学）；Yanshan University（燕山大学）
We propose predictive models that estimate GBM patients' health status of one-year after treatments (Classification task), predict the long-term prognosis of GBM patients at an individual level (Survival task). We used total of 467 GBM patients' clinical profile consists of 13 features and two follow-up dates. For baseline models of random forest classifier(RFC) and random survival forest model (RSF), we introduced generalized linear model (GLM), support vector machine (SVM) and Cox proportional hazardous model (COX), accelerated failure time model (AFT) respectively. After preprocessing and p…
Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests
Department of Mathematics and Physics, University of Campania Luigi Vanvitelli（数学与物理系，坎帕尼亚卢igi vanvitelli大学）；Department of Economics, Statistics and Business, Universitas Mercatorum（经济学、统计学与商业系，默卡托鲁姆大学）
JD Intelligent Cities Research（京东智能城市研究院）；JD Intelligent Cities Business Unit（京东智能城市事业部）；University of Science and Technology of China（中国科学技术大学）；Beijing Normal University（北京师范大学）
Most real-world data are scattered across different companies or government organizations, and cannot be easily integrated under data privacy and related regulations such as the European Union's General Data Protection Regulation (GDPR) and China' Cyber Security Law. Such data islands situation and data privacy & security are two major challenges for applications of artificial intelligence. In this paper, we tackle these challenges and propose a privacy-preserving machine learning model, called Federated Forest, which is a lossless learning model of the traditional random forest method, i.e., …
Batch effects can impair federated learning in multi-center omics studies
Institute for Computational Systems Biomedicine, University of Hamburg（汉堡大学计算系统生物医学研究所）；Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich（慕尼黑技术大学生命科学学院蛋白质组学与生物分析系）；Department of Mathematics and Computer Science, University of Southern Denmark（丹麦南部大学数学与计算机科学系）；Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔兰根-纽伦堡大学生物医学网络科学实验室）；Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich（慕尼黑技术大学生命科学学院系统生物学数据科学）；Institute of Clinical Molecular Biology (IKMB), Kiel University and University Medical Center Schleswig-Holstein（基尔大学与石勒苏益格-荷尔斯泰因大学医学中心临床分子生物学研究所）
Breiman's random forest (RF) can be interpreted as an implicit kernel generator,where the ensuing proximity matrix represents the data-driven RF kernel. Kernel perspective on the RF has been used to develop a principled framework for theoretical investigation of its statistical properties. However, practical utility of the links between kernels and the RF has not been widely explored and systematically evaluated.Focus of our work is investigation of the interplay between kernel methods and the RF. We elucidate the performance and properties of the data driven RF kernels used by regularized lin…
Comparison of the Cox proportional hazards model and Random Survival Forest algorithm for predicting patient-specific survival probabilities in clinical trial data
University of Augsburg（奥格斯堡大学）；University of Reading（雷丁大学）
Federated unsupervised random forest for privacy-preserving patient stratification
Institute for Medical Informatics, Statistics and Documentation Medical University Graz（格拉茨医科大学医学信息学、统计学与文献学研究所）；Department of Pure and Applied Sciences, University of Urbino（乌尔比诺大学纯科学与应用科学系）
Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II …
