Federated Transfer Learning.The need to protect data privacy and improve threat intelligence in the constantly changing field of cybersecurity has prompted the investigation of novel approaches. This research presents, within the scope of privacy-preserving cyber security, a decentralized method of threat...
Federated Learning (FL), a secure and emerging distributed learning paradigm, has garnered significant interest in the Internet of Things (IoT) domain. How... Y Cao,J Zhang,Y Zhao,... - 《IEEE Transactions on Information Forensics & Security》 被引量: 0发表: 0年 Ensuring Fairness and Gra...
We employ federated learning to improve models without exposing raw data, enabling distributed dataset training. Blockchain plays a crucial role by establishing transparent and immutable data access records, thereby enhancing security and accountability, a particularly critical aspect in healthcare where ...
联邦强化学习(Federated Reinforcement Learning):FRAMU采用了联邦学习和强化学习的结合方式,以分布式的形式在多个节点上训练模型,同时使用强化学习策略来优化决策过程。这种方法不仅减少了通信成本,还增强了隐私保护。 FedAvg机制(Federated Averaging):为了实现个性化的遗忘过程,FRAMU框架采用了FedAvg机制来聚合来自不同参与者...
Federated learning (FL) enables resource-constrained node devices to learn a shared model while keeping the training data local. Since recent research has ... zhenghaibin,ChenJinyin,LiuTao,... - 《Acm Transactions on Privacy & Security》 被引量: 0发表: 2023年 Turning Privacy-preserving Mechanis...
The first experiment utilized traditional ML models for data analysis, while the second employed the proposed Federated Deep Learning (FDL) framework. The aim was to compare the performance and efficiency of the two approaches in handling data from the IoT sensors. The IoT system is composed of ...
This method allows high-quality models to be trained in relatively few rounds of communication, the principal constraint for federated learning. The key insight is that despite the non-convex loss functions we optimize, parameter averaging over updates from multiple clients produces surprisingly good ...
Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights, accuracy is significantly reduced in unstable network environments. In this study, we propose...
Federated learning (FL) is an alternative to centralized machine learning (ML) that builds a model across multiple decentralized edge devices (a.k.a. workers) that own the training data. This has two advantages: i) the data used for training are not uploaded to the server and ii) the ser...
Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review 2023, Knowledge-Based Systems Citation Excerpt : Due to the decentralized approach of blockchain technology, it is highly used for the secure exchange of local models in FL systems. The security level...