Results 41 to 50 of about 223 (114)
Stable federated learning method for low-altitude IoT networks based on election strategy [PDF]
The deep integration of UAV and Internet of things (IoT) transmits a large amount of sensitive data in the air-to-ground intelligent network, posing a serious risk of privacy leakage.
BAI Tianshui +4 more
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智能科学与技术学报纵向联邦学习（vertical federated learning，VFL）常用于高风险场景中的跨领域数据共享，用户需要理解并信任模型决策以推动模型应用。现有研究主要关注VFL中可解释性与隐私之间的权衡，未充分满足用户对模型建立信任及调优的需求。为此，提出了一种基于人在回路（human-in-the-loop，HITL）的纵向联邦学习解释方法（explainable vertical federated learning based on human-in-the-loop，XVFL-HITL ...
李晓欢, 郑钧柏, 康嘉文, 叶进, 陈倩
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Survey on Byzantine attacks and defenses in federated learning [PDF]
Federated learning as an emerging distributed machine learning, can solve the problem of data islands. However, due to the large-scale, distributed nature and strong autonomy of local clients, federated learning is extremely vulnerable to Byzantine ...
HUANG Mei +4 more
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Node selection method in federated learning based on deep reinforcement learning [PDF]
, 2021 To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection ...
Liandong CHEN +4 more
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Multi-key homomorphic proxy re-encryption scheme based on NTRU and its application [PDF]
, 2021 To improve the practicability of homomorphic encryption in the application of multi-user cloud computing, a NTRU-based multi-key homomorphic proxy re-encryption (MKH-PRE) scheme was constructed.Firstly, a new form of NTRU-based multi-key ciphertext was ...
Chunfu JIA, Ruiqi LI, Yafei WANG
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Communication-efficient model pruning for federated learning in mobile edge computing [PDF]
In the mobile edge computing scenario, the distributed architecture of federated learning allows the edge server and mobile terminals to cooperatively train the deep model, without necessitating sharing of local data across the mobile terminals.
HU Haifeng +3 more
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High-performance federated continual learning algorithm for heterogeneous streaming data [PDF]
, 2023 Aiming at the problems of poor model performance and low training efficiency in training streaming data of AI models that provide intelligent services, a high-performance federated continual learning algorithm for heterogeneous streaming data (FCL-HSD ...
Hui JIANG +4 more
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Pilot spoofing detection algorithm for edge nodes based on heterogeneous pilot energy estimation [PDF]
, 2023 For the federated learning scenarios with edge-end cooperation, edge servers and device terminals update their models and exchange gradient parameters frequently, and hence eavesdroppers can manipulate channel estimation through pilot spoofing to ...
Qingyong DENG, Shiguo WANG, Shujuan TIAN
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Overview of anomaly detection techniques for industrial Internet of things [PDF]
, 2022 In view of the differences of existing anomaly detection methods and the applicability when applied to security protection of the industrial Internet of things (IIoT), based on technical principles, the network anomaly detection papers published from ...
Haili SUN +4 more
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智能科学与技术学报, 2019 概述了联邦可视化的概念、框架、方法与应用。联邦可视化框架能够在不进行数据整合的情况下，针对具体任务和特定场景进行加密训练，得出反映全体数据特征的可视化模型。联邦可视化是联邦学习框架在可视化领域的拓展应用，主要强调在保障数据隐私的前提下，互利共赢的联邦协作方式在对多数据源数据进行可视分析方面的应用，以打破各领域、各行业的数据壁垒，实现数据与知识的共享。
魏雅婷, 王智勇, 周舒悦, 陈为
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