主题: Federated Learning in Healthcare 日期: 2023-06-07 08:47:33 录制文件:https://meeting.tencent.com/v2/cloud-record/share?id=ec65d257-69ab-4807-b670-9312fb2a5bae&from=3 会议核心内容:首先付老师介绍了联邦学习,然后讨论联邦学习中的Non-IID问题并且从3个角度去解决该问题,最后介绍了一个开源平...
Federated Learning in Healthcare 内容介绍 Federated learning (FL) is an emerging distributed machine learning paradigm that leverages decentralized data from multiple clients to jointly train a shared global model under the coordination o...
报告嘉宾:李霄霄 (The University of British Columbia)报告时间:2022年07月06日 (星期三)晚上20:00 (北京时间)报告题目:Federated learning for healthcare: from theory to practice报告人简介:Dr. Xiaoxiao Li is an Assistant Professor at the D, 视频播放量 2239
Tips 联邦学习的起源:The term “federated learning” is not new. In 1976, Patrick Hill, a philosophy professor, first developed the Federated Learning Community (FLC) to bring people together to jointly learn, which helped students overcome the anonymity and isolation in large research universities ...
Multimodal Federated Learning in Healthcare: a Review J Thrasher, A Devkota, P Siwakotai, R Chivukula, P Poudel, C Hu, B Bhattarai, P Gyawali (2023) arxiv.org/abs/2310.0965 A Survey of Advances in Multimodal Federated Learning with Applications G Barry, E Konyar, B Harvill, C Johnstone...
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 F...
This paper explores the security aspects of federated learning applications in medical image analysis. Current robustness-oriented methods like adversarial training, secure aggregation, and homomorphic encryption often risk privacy compromises. The central aim is to defend the network against potential ...
联邦迁移学习 个性化:FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare 目录结构略有不同 概览 本文提出了一个用于可穿戴设备的联邦迁移学习框架FedHealth,用于针对不同用户进行个性化健康服务。FedHealth的思想大致如下:1)首先通过公共数据集(源域数据)在服务器上训练一个初始的云模型;2)之后...
Federated learning (FL) has enabled training models collaboratively from multiple data owning parties without sharing their data. Given the privacy regulations of patient's healthcare data, learning-based systems in healthcare can greatly benefit from privacy-preserving FL approaches. However, typical mo...
L. Putting the data before the algorithm in big data addressing personalized healthcare. NPJ Digit. Med. 2, 78 (2019). Article PubMed PubMed Central Google Scholar Thrall, J. H. et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and ...