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Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application

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arXiv 2024年

作者： Mukherjee, Anwesha Buyya, Rajkumar Department of Computer Science Mahishadal Raj College West Bengal Mahishadal721628 India Laboratory School of Computing and Information Systems The University of Melbourne VIC3052 Australia

Federated learning has become an emerging technology for data analysis for IoT applications. This paper implements centralized and decentralized federated learning frameworks for crop yield prediction based on Long Short-Term Memory Network. For centralized federated learning, multiple clients and one server is considered, where the clients exchange their model updates with the server that works as the aggregator to build the global model. For the decentralized framework, a collaborative network is formed among the devices either using ring topology or using mesh topology. In this network, each device receives model updates from the neighbour devices, and performs aggregation to build the upgraded model. The performance of the centralized and decentralized federated learning frameworks are evaluated in terms of prediction accuracy, precision, recall, F1-Score, and training time. The experimental results present that ≥97% and >97.5% prediction accuracy are achieved using the centralized and decentralized federated learning-based frameworks respectively. The results also show that the using centralized federated learning the response time can be reduced by ∼75% than the cloud-only framework. Finally, the future research directions of the use of federated learning in crop yield prediction are explored in this paper. © 2024, CC BY.

关键词： Federated learning

PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis

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arXiv 2024年

作者： Huang, Xinlei Ma, Zhiqi Meng, Dian Liu, Yanran Ruan, Shiwei Sun, Qingqiang Zheng, Xubin Qiao, Ziyue School of Computing and Information Technology Great Bay University Dongguan523000 China Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems Dongguan523000 China

Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal omics methods due to their ability to model semantic relations between sequencing spots. However, the fixed KNN graph fails to capture the latent semantic relations hidden by the inevitable data perturbations during the biological sequencing process, resulting in the loss of semantic information. In addition, the common lack of spot annotation and class number priors in practice further hinders the optimization of spatial multi-modal omics models. Here, we propose a novel spatial multi-modal omics resolved framework, termed PRototype-Aware Graph Adaptative Aggregation (PRAGA). PRAGA constructs a dynamic graph to capture latent semantic relations and comprehensively integrate spatial information and feature semantics. The learnable graph structure can also denoise perturbations by learning cross-modal knowledge. Moreover, a dynamic prototype contrastive learning is proposed based on the dynamic adaptability of Bayesian Gaussian Mixture Models to optimize the multi-modal omics representations for unknown biological priors. Quantitative and qualitative experiments on simulated and real datasets with 7 competing methods demonstrate the superior performance of PRAGA. Code is available at https://***/Xubin-s-Lab/PRAGA. © 2024, CC BY-NC-ND.

关键词： Contrastive Learning

A Cross-Platform Instant Messaging User Association Method Based on Spatio-temporal Trajectory 8th

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A Cross-Platform Instant Messaging User Association Method B...

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Zhou, Pei Luo, Xiangyang Du, Shaoyong Li, Lingling Yang, Yang Liu, Fenlin School of Cyber Science and Engineering Zhengzhou University Zhengzhou450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Intelligent Engineering Zhengzhou University of Aeronautics Zhengzhou450000 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore

ISBN: (纸本)9783031067600

The current research on cross-platform instant messaging user association is mainly divided into two categories: based on user attributes and based on user behavior. Methods based on user attributes mainly identify users through multiple attributes such as user name and multi-platform user information association based on cell phone number, but user association is not possible when multi-platform user information is inconsistent and users do not grant their address book permissions. Methods based on user behavior mainly calculate the similarity between user trajectories features such as geographic location frequency and co-occurrence, but this method lacks the user’s information, which leads to the inability to fully excavate the sequential features of the trajectory and affects the accuracy of trajectory matching. In order to solve this problem, this paper proposes a cross-platform instant messaging user association algorithm based on temporal trajectories (CPTrajst). We firstly place probes in the area where the target may appear so as to obtain user information, gets user trajectory, then processes the trajectory and performs two trajectory matches, finally associate users of different platforms whose trajectories match, thus increasing the accuracy and reliability of cross-platform instant messaging user association. We conducts specific experiments for users of WeChat and Momo, the most commonly used instant messengers in China. The results show that we can achieve reliable association for these two types of instant messaging users and the user association accuracy can reach 99.5%, which is better than the existing user association algorithms based on trajectory matching. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

Automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

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arXiv 2023年

作者： Zheng, Chuanpan Qi, Jianzhong Fan, Xiaoliang Chen, Chaochao Wang, Cheng Chen, Longbiao Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University China School of Computing and Information Systems University of Melbourne Australia College of Computer Science and Technology Zhejiang University China

Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires inductive learning. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://***/zhengchuanpan/INCREASE. Copyright © 2023, The Authors. All rights reserved.

关键词： Kriging

MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attention

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arXiv 2025年

作者： Yang, Rui Wu, Xiaojun He, Shengfeng The School of Computer Sciences Shaanxi Normal University Xi'An China The School of Computer Science The Key Laboratory of Intelligent Computing and Service Technology for Folk Song of the Ministry of Culture and Tourism Shaanxi Normal University Xi'An710119 China The School of Computing and Information Systems Singapore Management University Singapore

Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced sketch perception. At its core, MixSA employs a mixture-of-self-attention technique, which manipulates self-attention layers by substituting the keys and values with those from reference sketches. This allows for the seamless integration of brushstroke elements into initial outline images, offering precise control over texture density and enabling interpolation between styles to create novel, unseen styles. By aligning brushstroke styles with the texture and contours of colored images, particularly in late decoder layers handling local textures, MixSA addresses the common issue of color averaging by adjusting initial outlines. Evaluated with various perceptual metrics, MixSA demonstrates superior performance in sketch quality, flexibility, and applicability. This approach not only overcomes the limitations of existing methods but also empowers users to generate diverse, high-fidelity sketches that more accurately reflect a wide range of artistic expressions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image texture

Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

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Personalized Federated Learning with Collaborative Aggregati...

4th International Conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

DRL-based STAR-RIS-Assisted ISAC Secure Communications

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DRL-based STAR-RIS-Assisted ISAC Secure Communications

2023 International Conference on Ubiquitous Communication, Ucom 2023

作者： Zhu, Zhengyu Gong, Mengfei Chu, Zheng Xiao, Pei Sun, Gangcan Mi, De He, Ziming Tong, Fei School of Electrical and Information Engineering Zhengzhou University China Southeast University National Mobile Communications Research Laboratory China Institute for Communication Systems University of Surrey United Kingdom School of Computing and Digital Technology Birmingham City University United Kingdom Samsung Electronics Samsung Cambridge Solution Centre United Kingdom

ISBN: (纸本)9798350340433

In this paper, we explore a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-Assisted integrated sensing and communication (ISAC) secure communication system. The average long-Term security rate of the legitimate user (LU) is maximized by jointly designing the receive filters and transmit beamforming of the base station (BS), and the transmitting and reflecting coefficients of STAR-RIS, and in the meantime, guaranteeing the lower bound of echo signal-To-noise ratio (SNR) and the achievable rate of LU constraint. We propose to apply two deep reinforcement learning (DRL) algorithms to solve the complex non-convex problem and maximize the long-Term benefits of the system by optimizing the BS beamforming and STAR-RIS phase shifts. The simulation results thoroughly evaluate the performance of two DRL algorithms and demonstrate that STAR-RIS outperforms the conventional reconfigurable intelligent surface (RIS) in comparison with two benchmarks. © 2023 IEEE.

关键词： Reinforcement learning

MUT: Human-in-the-Loop Unit Test Migration

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MUT: Human-in-the-Loop Unit Test Migration

International Conference on Software Engineering (ICSE)

作者： Yi Gao Xing Hu Tongtong Xu Xin Xia David Lo Xiaohu Yang The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Huawei Hangzhou China School of Computing and Information Systems Singapore Management University Hangzhou China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Test migration, which enables the reuse of test cases crafted with knowledge and creativity by testers across various platforms and programming languages, has exhibited effectiveness in mobile app testing. However, unit test migration at the source code level has not garnered adequate attention and exploration. In this paper, we propose a novel cross-language and cross-platform test migration methodology, named MUT, which consists of four modules: code mapping, test case filtering, test case translation, and test case adaptation. MUT initially calculates code mappings to establish associations between source and target projects, and identifies suitable unit tests for migration from the source project. Then, MUT's code translation component generates a syntax tree by parsing the code to be migrated and progressively converts each node in the tree, ultima tely generating the target tests, which are compiled and executed in the target project. Moreover, we develop a web tool to assist developers in test migration. The effectiveness of our approach has been validated on five prevalent functional domain projects within the open-source community. We migrate a total of 550 unit tests and submitted pull requests to augment test code in the target projects on GitHub. By the time of this paper submission, 253 of these tests have already been merged into the projects (including 197 unit tests in the Luliyucoordinate-LeetCode project and 56 unit tests in the Rangerlee-HtmlParser project). Through running these tests, we identify 5 bugs, and 2 functional defects, and submitted corresponding issues to the project. The evaluation substantiates that MUT's test migration is both viable and beneficial across programming languages and different projects.

关键词： Computer languages Codes Filtering Source coding Syntactics Human in the loop Mobile applications