IEEE Networking Letters最新文献

IEEE Networking Letters Author Guidelines IEEE网络通讯作者指南

IEEE Networking Letters Pub Date : 2025-06-17 DOI: 10.1109/LNET.2025.3575217

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IEEE Networking Letters Society Information IEEE网络通讯协会信息

IEEE Networking Letters Pub Date : 2025-06-17 DOI: 10.1109/LNET.2025.3575219

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IEEE Networking Letters Publication Information IEEE网络通讯出版信息

IEEE Networking Letters Pub Date : 2025-06-17 DOI: 10.1109/LNET.2025.3575223

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Post-Quantum Secure Blockchain-Based Federated Learning Framework for Healthcare Analytics 后量子安全基于区块链的医疗保健分析联邦学习框架

IEEE Networking Letters Pub Date : 2025-04-22 DOI: 10.1109/LNET.2025.3563434

Daniel Commey;Sena G. Hounsinou;Garth V. Crosby

{"title":"Post-Quantum Secure Blockchain-Based Federated Learning Framework for Healthcare Analytics","authors":"Daniel Commey;Sena G. Hounsinou;Garth V. Crosby","doi":"10.1109/LNET.2025.3563434","DOIUrl":"https://doi.org/10.1109/LNET.2025.3563434","url":null,"abstract":"The growth of IoT in healthcare generates massive sensitive data. This necessitates a secure and privacy-preserving distributed network to transport and process the data. Federated learning (FL) offers privacy-preserving model training, while blockchain ensures data integrity through transparency and immutability. Yet, quantum computing threatens cryptographic schemes like ECDSA, endangering long-term data confidentiality. This paper integrates post-quantum cryptography (PQC) with blockchain-based FL for healthcare analytics. We evaluate three signature-based PQC algorithms—Falcon, Dilithium (ML-DSA-65), and SPHINCS+ (SPHINCS+-SHA2-128s)—to assess their impact on blockchain transaction costs and latency. Benchmarks on a local Ethereum testnet show that lattice-based schemes, particularly ML-DSA-65, achieve verification under 10 ms with acceptable gas costs. Our findings indicate that smart contract signature verification is the primary gas consumer, offering guidelines for deploying quantum-resistant FL systems. These findings justify and potentially create a foundation for building complete systems that integrate PQC into Blockchain-based FL systems.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 2","pages":"126-129"},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Toward Better QoT Estimation: An ML Architecture With Link-Level Embedding Layers 迈向更好的QoT估计：一个具有链接级嵌入层的机器学习体系结构

IEEE Networking Letters Pub Date : 2025-04-16 DOI: 10.1109/LNET.2025.3561336

Piotr Lechowicz;Carlos Natalino;Farhad Arpanaei;Stefan Melin;Renzo Diaz;Anders Lindgren;David Larrabeiti;Paolo Monti

{"title":"Toward Better QoT Estimation: An ML Architecture With Link-Level Embedding Layers","authors":"Piotr Lechowicz;Carlos Natalino;Farhad Arpanaei;Stefan Melin;Renzo Diaz;Anders Lindgren;David Larrabeiti;Paolo Monti","doi":"10.1109/LNET.2025.3561336","DOIUrl":"https://doi.org/10.1109/LNET.2025.3561336","url":null,"abstract":"Machine learning (ML) is emerging as a promising tool for estimating the Quality of Transmission (QoT) in optical networks, especially for unestablished lightpaths where traditional methods are limited. However, inaccuracies in ML-based QoT predictions—typically expressed in terms of generalized signal-to-noise ratio (GSNR)—can significantly affect network operation. Overestimation may lead to retransmissions due to overly aggressive modulation format choices, while underestimation results in underutilized spectral resources. To address this, we propose a novel ML architecture that incorporates an embedding layer for link-level features alongside path- and service-level inputs. Using data generated from an accurate analytical model, we show that our approach reduces prediction error by up to 34% compared to standard architectures. Simulated deployment scenarios further demonstrate operational benefits, with a 15.9% decrease in incorrect and a 34.8% reduction in overly conservative modulation format selections.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 2","pages":"122-125"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10966418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A Decentralized Matching Theory Framework to Match Data and Algorithms Providers 一个分散的匹配理论框架来匹配数据和算法提供者

IEEE Networking Letters Pub Date : 2025-04-14 DOI: 10.1109/LNET.2025.3560459

Chaouki Ben Issaid;Mehdi Bennis

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Critical Nodes Identification Algorithm Based on ResNet-CBAM 基于ResNet-CBAM的关键节点识别算法

IEEE Networking Letters Pub Date : 2025-03-22 DOI: 10.1109/LNET.2025.3572513

Xujie Li;Fei Shao;Ying Sun;Haotian Li;Jiayi Huang

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IEEE Networking Letters Author Guidelines IEEE网络通讯作者指南

IEEE Networking Letters Pub Date : 2025-03-18 DOI: 10.1109/LNET.2025.3544422

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