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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Comprehensive Architecture Search for Deep Graph Neural Networks

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IEEE Transactions on big data 2025年

作者： Dong, Yukang Pan, Fanxing Gui, Yi Jiang, Wenbin Wan, Yao Zheng, Ran Jin, Hai National Engineering Research Center for Big Data Technology Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Service Computing Technology and System Laboratory Wuhan430074 China Huazhong University of Science and Technology Cluster and Grid Computing Laboratory Wuhan430074 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

In recent years, Neural Architecture Search (NAS) has emerged as a promising approach for automatically discovering superior model architectures for deep Graph Neural Networks (GNNs). Different methods have paid attention to different types of search spaces. However, due to the time-consuming nature of training deep GNNs, existing NAS methods often fail to explore diverse search spaces sufficiently, which constrains their effectiveness. To crack this hard nut, we propose CAS-DGNN, a novel comprehensive architecture search method for deep GNNs. It encompasses four kinds of search spaces that are the composition of aggregate and update operators, different types of aggregate operators, residual connections, and hyper-parameters. To meet the needs of such a complex situation, a phased and hybrid search strategy is proposed to accommodate the diverse characteristics of different search spaces. Specifically, we divide the search process into four phases, utilizing evolutionary algorithms and Bayesian optimization. Meanwhile, we design two distinct search methods for residual connections (All-connected search and Initial Residual search) to streamline the search space, which enhances the scalability of CAS-DGNN. The experimental results show that CAS-DGNN achieves higher accuracy with competitive search costs across ten public datasets compared to existing methods. © 2015 IEEE.

关键词： Neural network models

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KNAS-TP: Domain Knowledge-driven Automated Construction of Uplink and Downlink Throughput Predictor in 5G 16

KNAS-TP: Domain Knowledge-driven Automated Construction of U...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Yang, Yuqian Zhao, Cong Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi'an China

ISBN: (纸本)9798331506322

The advent of 5G networks marks a revolutionary shift in wireless communications, offering unprecedented improvements in speed, latency, and connectivity. Accurate uplink and downlink throughput prediction is crucial for 5G network Operations and Maintenance (O&M) tasks, yet existing methods constructing intuitive predictors without structural optimization often fail to adapt to dynamic 5 G environments due to the lack of effective utilization of domain knowledge. To address this, we present KNAS-TP, the first domain knowledge-driven automated construction method for 5G throughput prediction based on Neural Architecture Search (NAS) and the Attention mechanism. KNAS-TP incorporates domain knowledge in NAS search space establishment and loss function definitions, which includes the match degree evaluation of the complexity of the searched neural network structure and corresponding knowledge. Additionally, KNAS-TP enhances model using an Attention mechanism to captures the uneven impacts of deep factors, thereby improving final model training and prediction accuracy. Extensive experimental results demonstrate that our model outperforms all comparative methods, achieving up to 8.5 × lower throughput prediction MSE. The integration of domain knowledge is essential for robust and accurate throughput predictions, validating the effectiveness of our methodology in complex 5G scenarios. © 2024 IEEE.

关键词： 5G mobile communication systems

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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Science China(Information Sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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Predicting the Risk of Incident Cardiovascular Disease Using Retinal Vessel Caliber Measured by Artificial Intelligence in a Chinese Community-Based Population

SSRN

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SSRN 2025年

作者： Luo, Xingyu Yang, Guangzhengao Fan, Fangfang Jiang, Yimeng Jia, Jia Zhao, Jie Chen, Zifan Yan, Shenshen She, Haicheng Zheng, Bo Zhang, Li Zhang, Yan Department of Cardiology Peking University First Hospital Beijing China Institute of Cardiovascular Disease Peking University First Hospital Beijing China Center for Data Science Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Changsha Institute for Computing and Digital Economy Beijing China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Science Key Lab Beijing China

Background: The predictive value of retinal vessel caliber, measured through artificial intelligence (AI), for incident cardiovascular disease (CVD) risk remains to be explored. Our study aimed to investigate the association between AI-measured retinal vessel caliber and the risk of incident CVD in a Chinese community-based population. Methods: We conducted a prospective cohort study on subjects from a Beijing community in China who were free of prevalent stroke or myocardial infarction and underwent retinal photography at baseline. By deep-learning algorithm, retinal vessel caliber measurements, including central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE), and arteriolar-to-venular ratio (AVR), were obtained from 0.5 to 1 disc diameter from the optic disc margin. The primary endpoint was an incident major adverse cardiovascular event (MACE), defined as a composite of first non-fatal stroke, first non-fatal acute myocardial infarction (AMI), and cardiovascular death. The secondary endpoints included first stroke, first AMI, and cardiovascular death. Findings: A total of 3,585 participants were included. The mean age was 56.49 ± 8.94 years, and 2335 (65.1%) were female. Prevalence of prior hypertension and diabetes was 1594 (44.5%) and 676 (18.9%), respectively. The mean values for CRAE, CRVE, and AVR were 190.83 ± 29.92 μm, 278.26 ± 36.34 μm, and 0.69 ± 0.08, respectively. During the follow-up (median, 7.53 years), there were 293 cases of incident MACE (8.2%), 226 strokes (6.3%), 57 AMIs (1.6%), and 44 cardiovascular deaths (1.2%). The Kaplan–Meier curves showed significant differences in cumulative hazards of the primary endpoint among tertiles of CRAE, CRVE, and AVR. In Cox proportional-hazards models adjusting for covariates, CRAE was negatively associated with the risk of incident MACE (per 10 μm increase, hazard ratio [HR] = 0.95, 95% confidence interval [CI]: 0.92-0.99, P = 0.009) and the risk of incident stroke (per 10 μm

关键词： Risk assessment

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Massive data clustering by multi-scale psychological observations

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national Science Review 2022年第2期9卷 43-51页

作者： Shusen Yang Liwen Zhang Chen Xu Hanqiao Yu Jianqing Fan Zongben Xu National Engineering Laboratory of Big Data Analytics Xi'an Jiaotong University Industrial Artificial Intelligent Center Pazhou Laboratory Department of Mathematics and Statistics University of Ottawa Center for Statistics and Machine Learning Princeton University

Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all ***,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all ***,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive *** observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping *** validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and *** approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.

关键词： massive data clustering psychological observation Weber–Fechner law cognitive interpretability computational scalability

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AEFNet: Adaptive Scale Feature Based on Elastic-and-Funnel Neural Network for Healthcare Representation

AEFNet: Adaptive Scale Feature Based on Elastic-and-Funnel N...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Liu, Yang Wu, Jialun Wei, Yuhua Mao, Bing Li, Chen Gong, Tieliang Xi'An Jiaotong University School of Computer Science and Technology National Engineering Lab for Big Data Analytics Shaanxi Xi'an710049 China

ISBN: (纸本)9781665401265

Healthcare Representation learning has been a key element to achieving state-of-the-art performance on healthcare prediction. Recent advances based Electronic Healthcare Records(EHRs) are mostly devoted to extracting temporal progression patterns with temporal model and their variants. Although these works have shown excellent performances in healthcare prediction, the unified temporal pattern may not be suitable for individuals in all healthcare conditions. Moreover, some studies ususally introduce complex Deep Neural Networks models and medical prior knowledge to get compact representation, causing great computational burden. In this paper, we propose a general health care representation model, named AEFNet. We only leverage three simple convolution operations and a set of up and down sampling to ensure performance and model complexity equally, which achieves adaptively extract distinct individual key feature in a light manner. AEFNet can shrink and refine highly suitable scale information adaptively and comletely. Breaking traditional fixed convolution scale or multi-scale, AEFNet achieves scale adaptively to extract the most significant information and context relationship. Finally, We validate our method on the public dataset MIMIC-III, and the evaluation results indicate that our method can significantly outperform other remarkable baseline models. © 2021 IEEE.

关键词： Forecasting

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Optimizing the Copy-on-Write Mechanism of Docker by Dynamic Prefetching

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Tsinghua Science and Technology 2021年第3期26卷 266-274页

作者： Yan Jiang Wei Liu Xuanhua Shi Weizhong Qiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHuazhong University of Science and TechnologyWuhan 430074China

Docker,as a mainstream container solution,adopts the Copy-on-Write(CoW)mechanism in its storage *** mechanism satisfies the need of different containers to share the same ***,when a single container performs operations such as modification of an image file,a duplicate is created in the upper readwrite layer,which contributes to the runtime *** the accessed image file is fairly large,this additional overhead becomes *** we present the concept of Dynamic Prefetching Strategy Optimization(DPSO),which optimizes the Co W mechanism for a Docker container on the basis of the dynamic prefetching *** the beginning of the container life cycle,DPSO pre-copies up the image files that are most likely to be copied up later to eliminate the overhead caused by performing this operation during application *** experimental results show that DPSO has an average prefetch accuracy of greater than 78%in complex scenarios and could effectively eliminate the overhead caused by the CoW mechanism.

关键词： Docker container Copy-on-Write(CoW) storage driver prefetch strategy

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LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning 50th

LightDiC: A Simple yet Effective Approach for Large-scale Di...

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50th International Conference on Very Large data Bases, VLDB 2024

作者： Li, Xunkai Liao, Meihao Wu, Zhengyu Su, Daohan Zhang, Wentao Li, Rong-Hua Wang, Guoren Beijing Institute of Technology China Peking University National Engineering Laboratory for Big Data Analytics and Applications China

Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and deployment. Compared with undirected graphs, directed graphs (digraphs) fit the demand for modeling more complex topological systems by capturing more intricate relationships between nodes. While some directed GNNs have been introduced, their inspiration mainly comes from deep learning architectures, which lead to redundant complexity and computation, making them inapplicable to large-scale databases. To address these issues, we propose LightDiC, a scalable variant of the digraph convolution based on the magnetic Laplacian. Since topology-related computations are conducted solely during offline pre-processing, LightDiC achieves exceptional scalability, enabling downstream predictions to be trained separately without incurring recursive computational costs. Theoretical analysis shows that LightDiC achieves message passing based on the complex field, which corresponds to the proximal gradient descent process of the Dirichlet energy optimization function from the perspective of digraph signal denoising, ensuring its expressiveness. Experimental results demonstrate that LightDiC performs comparably well or even outperforms other SOTA methods in various downstream tasks, with fewer learnable parameters and higher efficiency. © 2024, VLDB Endowment. All rights reserved.

关键词： Directed graphs

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Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations

Inductive Relation Prediction with Logical Reasoning Using C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, Xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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