DeCaF/FAIR@MICCAI最新文献

Towards Sparsified Federated Neuroimaging Models via Weight Pruning 基于权值修剪的稀疏联邦神经成像模型

DeCaF/FAIR@MICCAI Pub Date : 2022-08-24 DOI: 10.48550/arXiv.2208.11669

Dimitris Stripelis, Umang Gupta, N. Dhinagar, G. V. Steeg, Paul M. Thompson, J. Ambite

{"title":"Towards Sparsified Federated Neuroimaging Models via Weight Pruning","authors":"Dimitris Stripelis, Umang Gupta, N. Dhinagar, G. V. Steeg, Paul M. Thompson, J. Ambite","doi":"10.48550/arXiv.2208.11669","DOIUrl":"https://doi.org/10.48550/arXiv.2208.11669","url":null,"abstract":"Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model pruning techniques have been designed in centralized settings to reduce inference times. Combining centralized pruning techniques with federated training seems intuitive for reducing communication costs -- by pruning the model parameters right before the communication step. Moreover, such a progressive model pruning approach during training can also reduce training times/costs. To this end, we propose FedSparsify, which performs model pruning during federated training. In our experiments in centralized and federated settings on the brain age prediction task (estimating a person's age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions. One surprising benefit of model pruning is improved model privacy. We demonstrate that models with high sparsity are less susceptible to membership inference attacks, a type of privacy attack.","PeriodicalId":347091,"journal":{"name":"DeCaF/FAIR@MICCAI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114183578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation Split- u - net:防止协同多模态脑肿瘤分割中分裂学习的数据泄漏

DeCaF/FAIR@MICCAI Pub Date : 2022-08-22 DOI: 10.48550/arXiv.2208.10553

H. Roth, Ali Hatamizadeh, Ziyue Xu, Can Zhao, Wenqi Li, A. Myronenko, Daguang Xu

引用次数: 5

Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images 组织病理学图像联邦学习中基于聚类的安全多方计算

DeCaF/FAIR@MICCAI Pub Date : 2022-08-21 DOI: 10.48550/arXiv.2208.10919

S. M. Hosseini, Milad Sikaroudi, Morteza Babaie, H. Tizhoosh

{"title":"Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images","authors":"S. M. Hosseini, Milad Sikaroudi, Morteza Babaie, H. Tizhoosh","doi":"10.48550/arXiv.2208.10919","DOIUrl":"https://doi.org/10.48550/arXiv.2208.10919","url":null,"abstract":". Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private pa-tient data for training. In FL, participant hospitals periodically exchange training results rather than training samples with a central server. How-ever, having access to model parameters or gradients can expose private training data samples. To address this challenge, we adopt secure multiparty computation (SMC) to establish a privacy-preserving federated learning framework. In our proposed method, the hospitals are divided into clusters. After local training, each hospital splits its model weights among other hospitals in the same cluster such that no single hospital can retrieve other hospitals’ weights on its own. Then, all hospitals sum up the received weights, sending the results to the central server. Fi-nally, the central server aggregates the results, retrieving the average of models’ weights and updating the model without having access to individual hospitals’ weights. We conduct experiments on a publicly available repository, The Cancer Genome Atlas (TCGA). We compare the performance of the proposed framework with diﬀerential privacy and federated averaging as the baseline. The results reveal that compared to diﬀerential privacy, our framework can achieve higher accuracy with no privacy leakage risk at a cost of higher communication overhead.","PeriodicalId":347091,"journal":{"name":"DeCaF/FAIR@MICCAI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129642494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions 基于坐标卷积的超声胸膜积液深度学习分割

DeCaF/FAIR@MICCAI Pub Date : 2022-08-05 DOI: 10.48550/arXiv.2208.03305

Germain Morilhat, Naomi Kifle, Sandy FinesilverSmith, B. Ruijsink, V. Vergani, Habtamu Tegegne Desita, Z. Desita, E. Puyol-Antón, A. Carass, A. King

引用次数: 1

Content-Aware Differential Privacy with Conditional Invertible Neural Networks 基于条件可逆神经网络的内容感知差分隐私

DeCaF/FAIR@MICCAI Pub Date : 2022-07-29 DOI: 10.48550/arXiv.2207.14625

Malte Tölle, U. Köthe, F. André, B. Meder, S. Engelhardt

{"title":"Content-Aware Differential Privacy with Conditional Invertible Neural Networks","authors":"Malte Tölle, U. Köthe, F. André, B. Meder, S. Engelhardt","doi":"10.48550/arXiv.2207.14625","DOIUrl":"https://doi.org/10.48550/arXiv.2207.14625","url":null,"abstract":"Differential privacy (DP) has arisen as the gold standard in protecting an individual's privacy in datasets by adding calibrated noise to each data sample. While the application to categorical data is straightforward, its usability in the context of images has been limited. Contrary to categorical data the meaning of an image is inherent in the spatial correlation of neighboring pixels making the simple application of noise infeasible. Invertible Neural Networks (INN) have shown excellent generative performance while still providing the ability to quantify the exact likelihood. Their principle is based on transforming a complicated distribution into a simple one e.g. an image into a spherical Gaussian. We hypothesize that adding noise to the latent space of an INN can enable differentially private image modification. Manipulation of the latent space leads to a modified image while preserving important details. Further, by conditioning the INN on meta-data provided with the dataset we aim at leaving dimensions important for downstream tasks like classification untouched while altering other parts that potentially contain identifying information. We term our method content-aware differential privacy (CADP). We conduct experiments on publicly available benchmarking datasets as well as dedicated medical ones. In addition, we show the generalizability of our method to categorical data. The source code is publicly available at https://github.com/Cardio-AI/CADP.","PeriodicalId":347091,"journal":{"name":"DeCaF/FAIR@MICCAI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122009089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Can collaborative learning be private, robust and scalable? 协作学习可以是私有的、健壮的和可扩展的吗?

DeCaF/FAIR@MICCAI Pub Date : 2022-05-05 DOI: 10.48550/arXiv.2205.02652

Dmitrii Usynin, Helena Klause, D. Rueckert, Georgios Kaissis

引用次数: 2

LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network LRH-Net:低资源心脏网络的多层次知识精馏方法

DeCaF/FAIR@MICCAI Pub Date : 2022-04-11 DOI: 10.1007/978-3-031-18523-6_18

Ekansh Chauhan, Swathi Guptha, Likith Reddy, R. Bapi

引用次数: 0

FedAP: Adaptive Personalization in Federated Learning for Non-IID Data FedAP:非id数据联邦学习中的自适应个性化

DeCaF/FAIR@MICCAI Pub Date : 1900-01-01 DOI: 10.1007/978-3-031-18523-6_2

Yousef Yeganeh, Azade Farshad, Johannes Boschmann, Richard Gaus, Maximilian Frantzen, N. Navab