This paper proposes Federated Learning (FL) based smar t 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Smar t医疗保健系统,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,不分享原始数据,而是将模型重量分量传送给矿工;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流程的延误,使示范中心的能源消耗和FL流程延迟,以便学习基于链框架的分布式保健数据的有效模型;我们提出一个分两个阶段的解决办法:首先,提供稳定的匹配式协会算法,以最大限度地发挥矿工和MCs的效用,然后利用在差异隐私权(DP)和区链技术下使用Sottachatic 梯根(SGD)算法解决损失最小化问题;此外,我们将块链技术纳入拟议的基于FL框架,提供耐软和分散式模型重量分享,通过模拟实际世界保健数据,比较其他最先进的技术,显示拟议模式的有效性。