Machine learning methods that protect privacy are now highly sought after in the big data and ubiquitous connection era. Federated Learning (FL) is a unique and revolutionary method that solves this problem by allowing machine learning models to be trained on several dispersed clients or devices ...
保障用户隐私（ensuring privacy）Federated learning 不需要在云端存储用户数据。但为避免用户隐私泄露，谷歌...
Liping Li, Wei Xu, Tianyi Chen, Georgios B. Giannakis, and Qing Ling, "RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets," Nov. 2018. Available:https://arxiv.org/abs/1811.03761 Le Trieu Phong, and Tran Thi Phuong, "Privacy-Preserving Deep...
初识Federated Learning 背景 设备中有很多数据,可以用来训练模型提高用户体验。但是数据通常是敏感或者庞大的。 隐私问题 数据孤岛:每个公司都有数据,淘宝有你的购买记录,银行有你的资金状况,它们不能把数据共享,都是自己训练自己有的数据,是一个个数据孤岛。 联邦学习的概念 联邦机器学习是一个机器学习框架,能有效...
A Comparative Analysis ofAggregation Methods inFederated Learning onMNIST Machine learning methods that protect privacy are now highly sought after in the big data and ubiquitous connection era. Federated Learning (FL) is a uniqu... Agarwal, Vedik,Chandnani, Chirag Jitendra,Kulkarni, Shlok Chetan,...
Federated learning includes mobile phones for cooperative learning and training and contains limited data on device. Federated learning allocates the machine learning development over to the node (mobile device). There are three kinds of federated learni
Model aggregation techniques in federated learning: A comprehensive survey 2024, Future Generation Computer Systems Show abstract A systematic review of federated learning: Challenges, aggregation methods, and development tools 2023, Journal of Network and Computer Applications Citation Excerpt : McMahan et...
researchers have proposed various communication compression methods aimed at reducing communication overhead in both federated learning and distributed machine learning. These methods aim to alleviate the burden of communication while maintaining or improving the overall performance of the federated learning ...
We numerically evaluate the performance as well as the privacy for both the estimation and learning problems, demonstrating the advantages of our proposed methods. 联邦学习的一个显着特征是(本地)客户端数据可能具有统计异质性。这种异质性激发了个性化学习的设计,其中通过协作来训练个人(个性化)模型。文献中...
Each client’s raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective. 翻译成人话就是: Federated Learning 是指一类机器学习问题。 这类问题有一个中心的服务器,加上很多个客户端。 整个过程中...