“3.2.2 One-epoch operation in blockchain-based asynchronous FL As depicted in Fig. 2, the operation of the device Di at the l-th epoch consists of the following ten steps:”(Feng 等, 2022, p. 4) 3.2.2 基于区块链的异步 FL 中的一个 epoch 操作 如图 2 所示,设备 Di 在第 l epoch...
[论文笔记]BAFL: A Blockchain-Based Asynchronous Federated Learning Framework melowlz 生活孤独,但不要失去理智和希望贡献 基于区块链技术提出异步联邦学习(AFL)策略,允许每个设备异步地上传本地模型。利用区块链技术防止服务器遭受单点攻击,保证数据的去中心化存储。 为了保证BAFL的效率,作者提出控制出块率的策略以...
blockchain-based federated learningclient selectionincentive mechanismsFederated learning is a privacy-preserving machine learning technique that trains models across multiple devices holding local data samples without exchanging them. There are many challenging issues in federated learning, such as coordinating...
2.1 Blockchain-based federated learning techniques Blockchain is a decentralized storage system that works without any central authority and stores data in the form of a list of blocks that are linked using the cryptographic hash of the previous block [19]. Data in the blockchain is stored in...
The proposed BFL-hIoT model uses a blockchain based smart contract for secure data transmission and federated learning to protect the privacy of patient data. The federated learning approach facilitates training the models by sharing the model parameters in a secured manner over the blockchain, ...
联邦学习论文阅读笔记11 FGFL: A blockchain-based fair incentive governor for Federated Learning 面对的问题:激励分配不均、攻击者欺骗 方法:提出FGFL模型。 1)设计了时间衰减SLM算法度量工作者声誉; 2)设计了基于梯度相似度的轻量级方法度量工作者贡献;...
However, inefficiency and vulnerability to poisoning attacks have slowed federated learning performance. To solve the above problems, a blockchain-based asynchronous federated learning framework (BAFL) is proposed to pursue both the security and efficiency. Blockchain ensures that data cannot be tampered...
Simultaneously, the returned model hash is stored in the main blockchain, ensuring traceability and accountability in the federated learning process. This synchronous and credit-based approach not only optimizes the training coordination within the local group but also enhances the overall efficiency and...
Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. F
Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical ...