区块链的区块中记录两类交易:数据检索交易和数据分享交易。 (2)联邦学习模块(federated learning module); 2.2流程 (1)数据请求节点向临近的超级节点发送一项数据请求(a data sharing request); (2)超级节点去区块链上检索该请求之前是否被处理过,如果处理过,到(3),否则到(4); (3)在缓存节点中找到对应的模型M...
鉴于组会汇报,特地挑了篇论文来简单学习一下,本文原文A Privacy-Preserving Federated Learning for Multiparty Data Sharing,Yin et al. IEEE Trans. 2021. 随着5G和移动计算的快速发展,社交计算和社交物联网(IoT)领域的深度学习服务在过去几年丰富了我们的生活。具有计算能力的移动设备和物联网设备可以随时随地加入...
One of the primary challenges is data privacy and sharing, as well as the need for diverse and extensive datasets to train advanced AI models. Federated data access and federated learning (FL) offer innovative solutions by allowing the analysis and extraction of insights from health data without ...
Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT,程序员大本营,技术文章内容聚合第一站。
and deploying machine learning (ML) models and artificial intelligence (AI) techniques involve simple data-sharing models. Data must be fused, cleaned, and integrated and then used to train and test the models. This procedure faces challenges related to individuals’ privacy and personal data protec...
federated learning; non-IID data; regularization; data sharing; machine learning1. Introduction 1.1. Research Questions With the development of Internet of Things (IoT) technology, smart devices such as cell phones and tablets are becoming more prevalent in people’s lives. These portable devices ...
Typically, this means sharing and then centralizing those data sets in one location, but this becomes a concern when the training involves sensitive data. Federated learning (FL) is a distributed machine learning (ML) approach that enables organizational collaboration without exposing sensitive data ...
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw data. However, model updates can be extremely large...
The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results ...
Federated Learning with Non-IID Data Abstract 联邦学习支持边缘受限的计算设备(例如移动电话和物联网设备)在保持训练本地数据的同时共享模型。用去中心化的方法训练模型保障了隐私、安全、可管理和经济效益。在这项工作中,我们关注的是当本地数据为non-IID时联邦学习的统计挑战。我们首先展示了联邦学习的准确性显著...