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.
翻译:本文提出基于联邦学习(FL)的智能保健系统,其中医疗中心(MCs)利用从病人收集的数据对当地模式进行培训,并将模型重量在不分享原始数据、不考虑隐私保护的情况下,在一个基于链链的稳健框架内向矿工发送,而不分享原始数据;我们提出优化问题,办法是最大限度地发挥效用,尽量减少损失功能,考虑到能源消耗和FL进程延误,以学习基于链链框架的分布式保健数据的有效模型;我们提出分两个阶段的解决办法:首先,提供稳定的匹配式联系算法,以最大限度地发挥矿工和MC的效用,然后利用采用利用不同隐私下的FL(DP)和区链技术的Stochacatic梯根算法(SGD)解决损失最小化问题;此外,我们将块链技术纳入拟议的FL框架,以提供耐缓冲和分散式的模型重量共享;通过模拟实际世界保健数据,比较其他先进技术,显示拟议模式的有效性。