论文标题
BP鱼：区块链和保护FL的智能医疗保健
BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare
论文作者
论文摘要
本文提出了基于联邦学习（FL）的智能医疗保健系统，其中医疗中心（MCS）使用患者收集的数据训练本地模型，并将模型权重以基于区块链的强大框架将原始数据发送给矿工，而无需共享原始数据，将隐私保护保存限制。我们通过最大化效用并最大程度地降低了MCS在基于区块链框架的基础的分布式医疗保健数据上学习有效模型的能源消耗和FL过程延迟，从而提出了一个优化问题。我们在两个阶段中提出了一个解决方案：首先，提供一种稳定的基于匹配的关联算法，以最大程度地提高矿工和MC的实用性，然后使用在差异隐私（DP）和区块链技术下使用FL的随机梯度下降（SGD）算法解决最小化损失。此外，我们合并了区块链技术，以在拟议的基于FL的框架中提供耐寒和分散的模型重量共享。通过对其他最先进技术的现实医疗保健数据进行仿真来显示拟议模型的有效性。
This paper proposes Federated Learning (FL) based smart healthcare system where Medical Centers (MCs) train the local model using the data collected from patients and send the model weights to the miners in a blockchain-based robust framework without sharing raw data, keeping privacy preservation into deliberation. We formulate an optimization problem by maximizing the utility and minimizing the loss function considering energy consumption and FL process delay of MCs for learning effective models on distributed healthcare data underlying a blockchain-based framework. We propose a solution in two stages: first, offer a stable matching-based association algorithm to maximize the utility of both miners and MCs and then solve loss minimization using Stochastic Gradient Descent (SGD) algorithm employing FL under Differential Privacy (DP) and blockchain technology. Moreover, we incorporate blockchain technology to provide tempered resistant and decentralized model weight sharing in the proposed FL-based framework. The effectiveness of the proposed model is shown through simulation on real-world healthcare data comparing other state-of-the-art techniques.
