通过数据分类等信息将其划分为不同community,方便进行federated learning; 3)数据requester不仅是提供request序列,同时还提供相应的input,我们模型仅输出input通过模型的结果,而非模型本身; 4)共识机制变为PoQ(quality)根据联盟链内委员节点测试模型的accuracy来决定其是否成为leader,从而具有新的block生成权; 5)根据machine...
据调研,[1] 提出一种基于区块链的联邦学习通用框架(Blockchained On-Device Federated Learning, BlockFL),其具体步骤如下: 1. 参数初始化:创建创世块,其包含随机初始化的全局参数等信息。2. 本地模型更新:每一个终端设备从新区块下载全局参数后更新本地模型参数。3. 本地模型上传:矿工和终端设备是绑定的(矿工...
BAFL区块链异步联邦学习框架论文笔记:“Instead of the traditional federal average (FedAvg) algorithm, this paper evaluates the participating rank and proportion of the local model trained in BAFL of …
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。
client selectionproximal policy optimizationFederated learning enables data owners in the Internet of Things(IoT)to collaborate in training models without sharing private data,creating new business opportunities for building a data market.However,in practical operation,there are still some problems with ...
联邦学习论文阅读:Federated meta-learning for recommendation 2018年fb的文章Federated meta-learning for recommendation的阅读笔记 想法 用元学习的方法解决少数据的问题,并用差分隐私保护用户的隐私性 这篇文章作者声称有两点创新,一是meta-learning在算法层面,二是用联邦学习保护用户隐私。但文章中的算法A与之前工作...
Federated Learning (FL) is a promising form of distributed machine learning that preserves privacy by training models locally without sharing raw data. While FL ensures data privacy through collaborative learning, it faces several critical challenges. These include vulnerabilities to reverse engineering, ...
blockchain federated learning image classification 1. Introduction The Industrial Internet of Things (IIoT) relates to the billions of physical devices (like connected instruments, sensors, etc.) globally which is currently interconnected to the internet without needing human-to-computer interaction/human...
federated learning has emerged as a promising solution because it can offer improved artificial intelligence (AI) models in a way that data privacy is maintained despite utilizing valuable data from client devices[5,6]. However, federated learning still faces challenges, including the lack of punitiv...
The research paper also presents a decentralized federated learning platform that successfully trained a CNN model on the MNIST dataset using blockchain technology. The platform enables multiple workers to train the model simultaneously while maintaining data privacy and security. The decentralized ...