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Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense

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IEEE Transactions on knowledge and data engineering 2025年第6期37卷 3372-3385页

作者： Li, Wenjie Fan, Kai Zhang, Jingyuan Li, Hui Lim, Wei Yang Bryan Yang, Qiang Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong

Federated Learning (FL) is a promising privacypreserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness of both clients and servers, particularly against curious or malicious adversaries. In this paper, we introduce a novel framework named Federated Learning with Low-Dimensional Update Representation and Proximity-Based defense (FLURP), designed to address privacy preservation and resistance to Byzantine attacks in distributed learning environments. FLURP employs LinfSample method, enabling clients to compute the l∞ norm across sliding windows of updates, resulting in a Low-Dimensional Update Representation (LUR). Calculating the shared distance matrix among LURs, rather than updates, significantly reduces the overhead of Secure Multi- Party Computation (SMPC) by three orders of magnitude while effectively distinguishing between benign and poisoned updates. Additionally, FLURP integrates a privacy-preserving, proximitybased defense mechanism utilizing optimized SMPC protocols to minimize communication rounds. Our experiments demonstrate FLURP's effectiveness in countering Byzantine adversaries with low communication and runtime overhead. FLURP offers a scalable framework for secure and reliable FL in distributed environments, facilitating its application in scenarios requiring robust data management and security. © 2025 IEEE.

关键词： Federated learning

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Fabrication-Tolerant High-Speed 5-Bit Silicon Optical True Time Delay Line in the O-Band

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Journal of Lightwave Technology 2025年第9期43卷 4358-4366页

作者： Ni, Ziheng Lu, Liangjun Wang, Yixuan Liu, Yuanbin Chen, Jianping Zhou, Linjie Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Key Lab of Navigation and Location Services Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

Fast reconfiguration time on the order of nanoseconds is crucial for microwave photonic beamformers, particularly in 5G/6G wireless communication systems. Most previously reported integrated optical true time delay lines (OTTDLs) exhibit a slow response time of tens of microseconds. In this study, we demonstrate an O-band high-speed 5-bit silicon OTTDL based on fabrication-tolerant electro-optic push-pull optical switches, which exhibit lower phase deviation and reduced insertion loss compared to conventional designs. The rising and falling times are 9 ns and 11 ns for the electro-optic switching, respectively. The insertion loss and crosstalk of our fabrication-tolerant high-speed switch element are 0.66 dB (0.74 dB) and −25.09 dB (−27.41 dB) at the bar (cross) state, respectively, at the wavelength of 1310 nm. For the 5-bit OTTDL, the initial phase variation is only ∼0.10π, which is one-sixth of that observed in the conventional design. Additionally, our proposed OTTDL features a low fiber-to-fiber insertion loss of 8.72 dB and a delay tuning range of 0-313.9 ps. The high-performance, fabrication-tolerant OTTDLs hold significant promise for future applications in fast beamforming. © 1983-2012 IEEE.

关键词： Fiber to the x

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How to Mitigate Information Loss in knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback

arXiv

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arXiv 2025年

作者： Huang, Manzong Bu, Chenyang He, Yi Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology Ministry of Education China School of Data Science William & Mary WilliamsburgVA United States

knowledge Graph (KG)-augmented Large Language Models (LLMs) have recently propelled significant advances in complex reasoning tasks, thanks to their broad domain knowledge and contextual awareness. Unfortunately, current methods often assume KGs to be complete, which is impractical given the inherent limitations of KG construction and the potential loss of contextual cues when converting unstructured text into entity-relation triples. In response, this paper proposes the Triple Context Restoration and Query-driven Feedback (TCR-QF) framework, which reconstructs the textual context underlying each triple to mitigate information loss, while dynamically refining the KG structure by iteratively incorporating query-relevant missing knowledge. Experiments on five benchmark question-answering datasets substantiate the effectiveness of TCR-QF in KG and LLM integration, where it achieves a 29.1% improvement in Exact Match and a 15.5% improvement in F1 over its state-of-the-art GraphRAG competitors. © 2025, CC BY.

关键词： knowledge graph

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Accelerating the Thermal Response of a III-V/Si3N4 Hybrid External Cavity Laser with Electronic-Photonic Co-Design

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Journal of Lightwave Technology 2025年第10期43卷 4871-4880页

作者： Han, keyi Lu, Liangjun Guo, Yuyao Li, Xinhang Xu, Weihan Chen, Jianpin Zhou, Linjie Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks The Shanghai Institute for Advanced Communication and Data Science Shanghai Key Lab of Navigation and Location Services Department of Electronic Engineering Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

We propose an electronic-photonic (EP) co-designed scheme to enhance the modulation speed of thermo-optic (TO) phase shifters. We establish a small signal analysis model to optimize the TO phase shifter by compensating its thermal-response-limited low-pass behavior with an electric high-pass equalization circuit, thus expanding the modulation bandwidth. To prove its potential, we demonstrate an EP-converged frequency-modulated continuous-wave (FMCW) laser comprising an InP/Si3N4 hybrid integrated external cavity laser (ECL) driving with an electric equalization circuit. The small signal bandwidth of the TO phase shifter inside the laser cavity is enhanced from 10 kHz to 360 kHz with the equalization circuit. By driving the TO phase shifter, we generate an FMCW signal with a chirp bandwidth of 943 MHz at a repetition rate of 100 kHz. The residual nonlinearity of the frequency chirp is reduced to 0.72% thanks to the pre-distortion technique, enabling a ranging precision of 8.1 cm over a 20 m distance. © 2025 IEEE.

关键词： Pulse repetition rate

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Microdroplet splitting and mixing by portable triboelectric nanogenerator

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Nano Research 2025年第2期18卷 741-750页

作者： Qi Gao Qin Xiang Wenkai Liu Mengru Ding Wei Long Jianlong Wang Xiaojun Cheng Hengyu Li Zhong Lin Wang Tinghai Cheng Guangzhou Institute of Blue Energy Knowledge CityHuangpu DistrictGuangzhou 510555China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of SciencesBeijing 101400China Faculty of Mechanical and Electrical Engineering Kunming University of Science and TechnologyKunming 650500China

Theadvancementofdigital microfluidics technology has been pivotal in academic research andengineering ***,the prevailing limitation is that traditional voltage sources generate an excess of Joule heat,adversely impacting droplet ***,the power supply equipment required by digital microfluidics limits its ***,we propose a self-powered microdroplet manipulation(SMDM)via triboelectric nanogenerator(TENG),which presents a capability for spliting and mixing different kinds of ***,SMDM is based on the electroosmotic flow principle,thereby enabling droplet spliting in the range of from 2 to 630μ***,for droplet spliting in the range of from 5 to 60μL,the TENG only requires a power output ranging from 2.704 to 6.084 *** addition,SMDM demonstrates proficiency in droplet mixing,which achieves complete mixing of 10μL droplets in 60 s and 30μL droplets in a mere 53 ***,leveraging the strengths of the TENG,a self-powered microdroplet manipulated system is designed for digital *** carries significant advantages over the traditional voltage source,including self-powered,low-Joule heat,increased safety and enhanced *** research provides a new solution for portable applications of digital microfluidics.

关键词： triboelectric nanogenerator microdroplet manipulation digital microfluidics droplet splitting droplet mixing

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LLMIdxAdvis: Resource-Efficient Index Advisor Utilizing Large Language Model

arXiv

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arXiv 2025年

作者： Zhao, Xinxin Li, Haoyang Zhang, Jing Huang, Xinmei Zhang, Tieying Chen, Jianjun Shi, Rui Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China ByteDance China

Index recommendation is essential for improving query performance in database management systems (DBMSs) through creating an optimal set of indexes under specific constraints. Traditional methods, such as heuristic and learning-based approaches, are effective but face challenges like lengthy recommendation time, resource-intensive training, and poor generalization across different workloads and database schemas. To address these issues, we propose LLMIdxAdvis, a resource-efficient index advisor that uses large language models (LLMs) without extensive fine-tuning. LLMIdxAdvis frames index recommendation as a sequence-to-sequence task, taking target workload, storage constraint, and corresponding database environment as input, and directly outputting recommended indexes. It constructs a high-quality demonstration pool offline, using GPT-4-Turbo to synthesize diverse SQL queries and applying integrated heuristic methods to collect both default and refined labels. During recommendation, these demonstrations are ranked to inject database expertise via in-context learning. Additionally, LLMIdxAdvis extracts workload features involving specific column statistical information to strengthen LLM’s understanding, and introduces a novel inference scaling strategy combining vertical scaling (via "Index-Guided Major Voting" and Best-of-N) and horizontal scaling (through iterative "self-optimization" with database feedback) to enhance reliability. Experiments on 3 OLAP and 2 real-world benchmarks reveal that LLMIdxAdvis delivers competitive index recommendation with reduced runtime, and generalizes effectively across different workloads and database schemas. Copyright © 2025, The Authors. All rights reserved.

关键词： Query languages

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Lightweight Dual Grouped Large-Kernel Convolutions for Salient Object Detection Network 31st

Lightweight Dual Grouped Large-Kernel Convolutions for Sali...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Liu, Jiajie Zhang, Zhibin Engineering Research Center of Ecological Big Data Ministry of Education Beijing China Key Laboratory of Wireless Networks and Mobile Computing Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9789819620609

Most existing Salient Object Detection (SOD) methods focus on achieving better performance, often resulting in models with a large number of parameters. However, there is limited research on lightweight models in this field. To address this gap, our goal is to maintain performance while reducing the number of network parameters. Thanks to the development of large-kernel convolutions in recent years, we have improved the U2Net as the base network by adding our lightweight dual large-kernel fusion module. Our module better utilizes the depth information of U2Net and, due to the large receptive field of large-kernel convolutions, better captures the relationships between image elements. This allows our network to remain lightweight while maintaining performance. We designed a large-kernel (DLK) fusion module and a lightweight dual grouped large-kernel Unet network (DGLKUNET). Our network uses SRUS (an improved Residual U-blocks module, RUS) to construct DGLKUNET that predicts image contours and labels. Compared to the base RUS network (U2Net), our network reduces the number of parameters by up to 70% and the computational cost by 40% while maintaining performance. Evaluation results on five datasets demonstrate the performance of our lightweight network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object recognition

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Improving Adaptive Runoff Forecasts in data-Scarce Watersheds Through Personalized Federated Learning 27th

Improving Adaptive Runoff Forecasts in Data-Scarce Watershe...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xie, Zaipeng Zhang, Xiangqin Wang, Yunfei Jie, Xuanyao Fang, Wenhao Cai, Yanping Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031781827

Runoff forecasting plays a crucial role in water resource management and flood mitigation, but it often faces significant challenges due to data deficiency and decentralized datasets. Inadequate hydrological data in many watersheds hinders the development of accurate prediction models, while organizational barriers and concerns about data privacy lead to information silos, preventing effective collaborative modeling. To address these issues, we propose FedHydroDSW, a novel personalized federated learning framework for runoff forecasting in data-scarce watersheds. Our approach enables decentralized model training across multiple organizations without requiring direct data sharing, thus preserving data privacy. FedHydroDSW incorporates unique model similarity metrics and parameter adaptation methods, utilizing insights from data-rich areas to refine forecasts in data-scarce regions. Experiments on the CAMELS dataset show that FedHydroDSW significantly outperforms standalone and conventional federated learning methods, with an average 30.1% increase in NSE, and reductions of 28% in RAE and 26.6% in RMSE across multiple data-scarce basins. By enabling secure cross-silo learning, our FedHydroDSW strategy paves the way toward more equitable runoff forecasting globally. The customized training and pattern recognition techniques represent major progress in deploying federated intelligence for impactful hydrological predictions. Our code is publicly available at https://***/xyjie37/FedHydroDSW. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Surface-enhanced Raman scattering on nanomaterials for cancer and pathogens diagnosis

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Nano Research 2025年第2期18卷 650-674页

作者： Jingjing Wu Baohong Zhang Lei Xu Yanyu Zhao Tao Chen Jie Lin Anran Li School of Engineering Medicine Beihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology of the People’s Republic of ChinaBeihang UniversityBeijing 100191China School of Optics and Photonics Beijing Institute of TechnologyBeijing 100089China Shanxi Lu'an Taihang Lubricant Technology Co. Ltd.Changzhi 046204China Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China

Surface-enhanced Raman scattering(SERS)is a non-invasive spectroscopic technique that provides specific chemical fingerprint information for biomarkers in cancer and pathogen ***,the SERS strategies are limited by the non-specific interactions between substrates and co-existing substances in biological matrices and the challenges of obtaining molecular fingerprint information from the complex vibrational *** recent years,the rapid development of novel substrates with high SERS activity has opened up new opportunities for their applications in cancer and pathogen *** aim of this review is to present the recent progress and perspectives of novel SERS-based substrates for cancer and pathogen diagnostic ***,we will introduce recently developed SERS-active nanomaterials and discuss the influencing factors of the SERS ***,the advantages of SERS in the diagnosis of cancer and pathogens will be ***,we will review the latest breakthroughs in cancer and pathogen detection research with SERS technology,as well as the new opportunities for SERS applications brought about by artificial intelligence(Al)*** addition,the novel microfluidic-SERS platforms for cancer and pathogens diagnosis will also be ***,we will summarize the challenges and future perspectives of SERS technology in the field of early cancer diagnosis and rapid pathogen *** is highly expected that this review could benefit a comprehensive understanding of the research status of the SERS-active nanomaterials and arouse the research enthusiasm for them,leading to accelerated clinical translation of SERS technology in cancer and pathogen diagnosis.

关键词： surface-enhanced Raman scattering(SERS) SERS-active nanomaterials cancer and pathogen diagnosis artificial intelligence(Al)technology

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Solidification modes and delta-ferrite of two types of 316L stainless steels:a combination of as-cast microstructure and HT-CLSM research

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Journal of Iron and Steel Research International 2025年第2期32卷 426-436页

作者： Yang Wang Chao Chen Xiao-yu Yang Zheng-rui Zhang Jian Wang Zhou Li Lei Chen Wang-zhong Mu College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan030024ShanxiChina Department of Materials Science and Engineering KTH Royal Institute of TechnologyStockholm10044Sweden College of Artificial Intelligence and Big Data for Medical Sciences Shandong First Medical University and Shandong Academy of Medical SciencesJinan250117ShandongChina College of Mechanical Engineering Taiyuan University of TechnologyTaiyuan030024ShanxiChina Key Laboratory of Electromagnetic Processing of Materials(Ministry of Education) Northeastern UniversityShenyang110819LiaoningChina

In 316L austenitic stainless steel,the presence of ferrite phase severely affects the non-magnetic properties.316L austenitic stainless steel with low-alloy type(L-316L)and high-alloy type(H-316L)has been *** microstructure and solidification kinetics of the two as-cast grades were in situ observed by high temperature confocal laser scanning microscopy(HT-CLSM).There are significant differences in the as-cast microstructures of the two 316L stainless steel *** L-316L steel,ferrite morphology appears as the short rods with a ferrite content of 6.98%,forming a dual-phase microstructure consisting of austenite and ***,in H-316L steel,the ferrite appears as discontinuous network structures with a content of 4.41%,forming a microstructure composed of austenite and sigma(σ)*** alloying elements in H-316L steel exhibit a complex distribution,with Ni and Mo enriching at the austenite grain ***-CLSM experiments provide the real-time observation of the solidification processes of both 316L specimens and reveal distinct solidification modes:L-316L steel solidifies in an FA mode,whereas H-316L steel solidifies in an AF *** differences result in ferrite and austenite predominantly serving as the nucleation and growth phases,*** solidification mode observed by experiments is similar to the thermodynamic calculation *** L-316L steel solidified in the FA mode and showed minimal element segregation,which lead to a direct transformation of ferrite to austenite phase(δ→γ)during phase transformation after ***,the H-316L steel solidified in the AF mode and showed severe element segregation,which lead to Mo enrichment at grain boundaries and transformation of ferrite into sigma and austenite phases through the eutectoid reaction(δ→σ+γ).

关键词： 316L austenitic stainless steel As-cast microstructure High-temperature confocal laser scanning microscopy Solidification mode Ferrite Characterization

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