标题： 基于区块链的个性化医疗中的隐私保护二阶联邦边缘学习 显示英文标题

标题： Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare

摘要： 联邦学习（FL）已经吸引了越来越多的关注，以缓解传统以云为中心的机器学习模型中的安全和隐私挑战，特别是在医疗生态系统中。 FL方法通过本地化策略训练全局模型，允许在边缘客户端级别进行独立操作。 由于每个边缘客户端的异构非独立同分布（non-iid）数据，传统的基于一阶的FL方法在个性化模型训练方面面临多个挑战。 最近，二阶FL方法在提高个性化模型训练的同时，维持了non-iid数据集的稳定性和一致性。 本研究提出并开发了一种基于优化FedCurv的可验证且可审计的优化二阶FL框架BFEL（区块链增强的联邦边缘学习），用于个性化医疗系统。 FedCurv通过整合Fisher信息矩阵中每个参数对每个客户端任务的重要性信息，有助于保留客户端特定的知识并在聚合过程中减少模型漂移。 此外，它在实现每个边缘客户端目标精度收敛所需的通信轮数最小化的同时，有效管理了non-iid和异构数据上的个性化训练。 以太坊模型聚合的加入确保了信任、可验证性和可审计性，而公钥加密增强了隐私和安全性。 利用Mnist、Cifar-10和PathMnist的数据集进行的联邦CNN和MLP实验结果证明了所提出的框架具有高效率和可扩展性。 显示英文摘要

摘要： Federated learning (FL) has attracted increasing attention to mitigate security and privacy challenges in traditional cloud-centric machine learning models specifically in healthcare ecosystems. FL methodologies enable the training of global models through localized policies, allowing independent operations at the edge clients' level. Conventional first-order FL approaches face several challenges in personalized model training due to heterogeneous non-independent and identically distributed (non-iid) data of each edge client. Recently, second-order FL approaches maintain the stability and consistency of non-iid datasets while improving personalized model training. This study proposes and develops a verifiable and auditable optimized second-order FL framework BFEL (blockchain-enhanced federated edge learning) based on optimized FedCurv for personalized healthcare systems. FedCurv incorporates information about the importance of each parameter to each client's task (through Fisher Information Matrix) which helps to preserve client-specific knowledge and reduce model drift during aggregation. Moreover, it minimizes communication rounds required to achieve a target precision convergence for each edge client while effectively managing personalized training on non-iid and heterogeneous data. The incorporation of Ethereum-based model aggregation ensures trust, verifiability, and auditability while public key encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing Mnist, Cifar-10, and PathMnist demonstrate the high efficiency and scalability of the proposed framework.