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FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous neural networks 51

FedClassAvg: Local Representation Learning for Personalized ...

引用

51st International conference on Parallel processing (ICPP)

作者： Jang, Jaehee Ha, Heonseok Jung, Dahuin Yoon, Sungroh Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Seoul Natl Univ Dept Elect & Comp Engn Interdisciplinary Program Artificial Intelligence Seoul South Korea

ISBN: (纸本)9781450397339

Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner without exchanging private data. However, many personalized federated learning algorithms assume that clients have the same neural network architecture, and those for heterogeneous models remain understudied. In this study, we propose a novel personalized federated learning method called federated classifier averaging (FedClassAvg). Deep neural networks for supervised learning tasks consist of feature extractor and classifier layers. FedClassAvg aggregates classifier weights as an agreement on decision boundaries on feature spaces so that clients with not independently and identically distributed (non-iid) data can learn about scarce labels. In addition, local feature representation learning is applied to stabilize the decision boundaries and improve the local feature extraction capabilities for clients. While the existing methods require the collection of auxiliary data or model weights to generate a counterpart, FedClassAvg only requires clients to communicate with a couple of fully connected layers, which is highly communication-efficient. Moreover, FedClassAvg does not require extra optimization problems such as knowledge transfer, which requires intensive computation overhead. We evaluated FedClassAvg through extensive experiments and demonstrated it outperforms the current state-of-the-art algorithms on heterogeneous personalized federated learning tasks.

关键词： neural networks Federated Learning Model Heterogeneity Resource Constraint Communication Efficient Representation Learning

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Research on face replacement detection based on deep learning

Research on face replacement detection based on deep learnin...

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2024 International conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Qi, Yue Shandong Institute of Commerce and Technology Shandong Province Jinan250001 China

ISBN: (纸本)9781510681514

With the development of artificial intelligence technology, the thresh- old of information tampering is reduced, and the quality of counterfeiting is increased. More and more fake information is difficult to distinguish. If such false information is widely disseminated, it will have a particular impact on society. It is essential to note that if face information is tampered with and widely distributed, it could lead to social unrest or damage an individual's reputation, resulting in trust crisis.A face tampering detection method combined with a self-attention mechanism is proposed to solve that traditional residual networks cannot capture the long-range dependencies between video frames and ignore local critical information. The face regions in the video are extracted firstly. Then the spatial features are extracted by the residual network, and the local critical information is learned by the self-attention block. Finally, the long-range dependencies of the spatial features are captured using the gated recurrent unit classification module. Experiments are conducted on the Face-Forensics++. The detection accuracy of the method is improved compared with several baseline algorithms, indicating that the methprocess effectively detect whether the face region in the video tampers. © 2024 SPIE.

关键词： Convolutional neural networks

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RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent neural networks 36

RNNs of RNNs: Recursive Construction of Stable Assemblies of...

引用

36th conference on neural Information processing Systems (NeurIPS)

作者： Kozachkov, Leo Ennis, Michaela Slotine, Jean-Jacques MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA Harvard Univ Div Med Sci Cambridge MA 02138 USA MIT Dept Mech Engn Cambridge MA 02139 USA Google AI Mountain View CA USA

ISBN: (纸本)9781713871088

Recurrent neural networks (RNNs) are widely used throughout neuroscience as models of local neural activity. Many properties of single RNNs are well characterized theoretically, but experimental neuroscience has moved in the direction of studying multiple interacting areas, and RNN theory needs to be likewise extended. We take a constructive approach towards this problem, leveraging tools from nonlinear control theory and machine learning to characterize when combinations of stable RNNs will themselves be stable. Importantly, we derive conditions which allow for massive feedback connections between interacting RNNs. We parameterize these conditions for easy optimization using gradient-based techniques, and show that stability-constrained 'networks of networks' can perform well on challenging sequential-processing benchmark tasks. Altogether, our results provide a principled approach towards understanding distributed, modular function in the brain.

关键词： Recurrent neural networks

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Deep Learning based UAV Assisted distributed Platform for Crowd Source Management 7

Deep Learning based UAV Assisted Distributed Platform for Cr...

引用

7th International conference on Signal processing and Information Security, ICSPIS 2024

作者： Ullah, Farman Sardar, Abdul Wasay Almeqbaali, Mariam Salem Saeed Aldhaheri, Meera Saeed Obaid Ali, Salama Obaid Altheeb Al United Arab Emirates University College of Information Technology Abu Dhabi Al-Ain17551 United Arab Emirates University College of Dublin Natural Computing Research & Applications Group and Lero Ireland

ISBN: (纸本)9798350368673

Person localization and crowd sourcing in aerial Images is a challenging problem and of great interest in many applications such as crowd safety and security management, people management in disasters, and person movement in an epidemic disease situation. However, the Aerial images have an issue of cluttered background, low-resolution quality, and various environment and lighting conditions, which increase complexity levels of localization and recognition. In this paper, we propose a deep learning-based approach on a Raspberry Pi-powered UAV platform. for the acquisition and localization of crowds considering the different orientations of the UAV and heterogeneous environmental factors. We focus on implementing object location and recognition with various constraints. We collected a large local environment dataset for better accuracy. We compared the performance of Haar-Cascade, template matching, YOLO, and convolutional neural network. The aforementioned results show that YOLO performs better for crowd detection and localization. © 2024 IEEE.

关键词： Template matching

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2nd IEEE International conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

2nd IEEE International Conference on Distributed Computing a...

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2nd IEEE International conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

ISBN: (纸本)9798350347456

The proceedings contain 295 papers. The topics discussed include: deep learning & computer vision integrated smart voting system;performance analysis of machine learning algorithms with hyperparameter tuning for diabetes prediction;transient analysis of motor terminal voltage, common mode voltage and bearing voltage in 2-level and multilevel PWM inverter fed induction motor with long cable;comparative analysis of phase/frequency detector in a complete PLL system;a research on various security aware mechanisms in multi-cloud environment for improving data security;network traffic virtualization using Wireshark and Google maps;cluster enabled routing algorithm for wireless sensor networks: an optimization and future prospective;detection and recognition of animals using Yolo algorithm;voltage improvement and loss reduction by placement and sizing of DG using grid oriented multi objective particle swarm optimization;prediction of heart disease using naive bayes and particle swarm optimization (PSO) method;lane detection using video processing for robot cars;convolutional neural network for printed circuit board verification;and multiple disease prediction based on user symptoms using machine learning algorithms.

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Encrypted Image Retrieval Scheme Based on neural network and LBP Mapping 29

Encrypted Image Retrieval Scheme Based on Neural Network and...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Wang, Lan Wang, Tian Wang, Zheng Jiangsu College of Finance and Accounting School of Ie Lianyungang222061 China

ISBN: (纸本)9798350330717

The privacy-preserving image retrieval research has been a hot issue for cyber-physical system security. Image encryption could effectively meet the growing privacy needs of users. However, the existing searchable encryption schemes are computationally complex, time-consuming, and have low retrieval accuracy. What's more, the image feature extraction requires multiple round communications between the cloud server and the image owner, which causes additional communication burdens. To solve these problems, a privacy-preserving image retrieval scheme combining deep learning technology and Local Binary Pattern (LBP) features is proposed. To protect images, block permutation, order-preserving encryption, and intra-block pixel substitution with multiple tables are used. The encrypted images would be uploaded to the cloud server and the LBP maps of encrypted images were calculated on the cloud side. Then, the neural network was trained by the encrypted LBP maps, and the image features were extracted from them with the trained neural network. The extracted features establish the image index. The Manhattan distance of features was utilized to measure the similarity between the stored images. Finally, the images similar to the query image were returned to the image owner. The security analysis and experimental results show that the proposed scheme is better than most existing schemes regarding security and retrieval accuracy. © 2023 IEEE.

关键词： Computation outsourcing cyber-physical system local binary pattern searchable encryption secure feature extraction

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Boosting the data processing speed by artificial neural network in distributed fiber-optic sensor 27

Boosting the data processing speed by artificial neural netw...

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27th International conference on Optical Fiber Sensors, OFS 2020

作者： Wang, Yifan Liu, Qingwen Li, Biaozhi Chen, Dian Li, He He, Zuyuan State Key Laboratory of Advanced Optical Communicate Systems and Networks Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai200240 China

The traditional corss-correlation algorithm is replaced by artificial neural network (ANN) in distributed fiber-optic sensor, and the data processing speed is boosted by 450 times with the nearly same SNR. © 2021... 详细信息

ISBN: (纸本)9781557523075

关键词： neural networks

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Topological Dependencies in Deep Learning for Mobile Edge: distributed and Collaborative High-Speed Inference 2

Topological Dependencies in Deep Learning for Mobile Edge: D...

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2nd International conference on Electronics and Renewable Systems, ICEARS 2023

作者： Abd Algani, Yousef Methkal Kumar, A Suresh Ala Walid, Md. Abul Balu, S. Velayutham, Priya Sasi Kumar, A. The Arab Academic College for Education in Israel-Haifa Department of Mathematics Israel Rathinam Technical Campus Department of Computer Science and Engineering Tamil Nadu Coimbatore India Khulna9203 Bangladesh Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamil Nadu Namakkal India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Inurture Education Solutions Pvt Ltd Bangalore India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Karnataka State India

ISBN: (纸本)9798350346640

Edge computing is now widely deployed across the globe. Although the Internet serves as the foundation for edge computing, the actual usefulness of this type of computing is seen when it is combined with the process of obtaining data from sensors and deriving relevant information from that data. It is predicted that in the not-too-distant era, most edge devices will be equipped with intelligent systems powered by deep learning. Unfortunately, in order to train, methods based on deep learning require a significant quantity of data of a high standard, and these methods are quite costly in terms of the amount of processing, memory, and power that they use. distributed deep neural networks, or DDNNs, are something suggested by using distributed computing hierarchies. The cloud, fog, and devices make up these tiers. Although a DDNN can facilitate the interpretation of a DNN in the cloud, it also allows for interpretation to be carried out quickly and precisely on the edges and on end devices by making use of shallow parts of the neural network. With the help of a scalable cloud-based infrastructure, a DDNN can expand both in terms of volume of its neural network and the number of users it serves around the world. For DNN applications, the distributed nature of DDNNs results in improvements to sensor fusion, fault tolerance in the system, and data privacy. In order to implement DDNN, first the portions of a DNN are mapped onto a dispersed computing structure. By learning both components together, the devices' need become limited for both connectivity and energy while the model provided the value of the selected features in the cloud. The final product includes provision for instinctive sensor fusion as well as fault tolerance that has been built directly into the system. This study demonstrates as a proof of concept that a DDNN may make use of the geographical variety of sensors to improve the accuracy of object detection and lower the cost of communication. The suggested

关键词： Edge computing

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Optimization Design of English Translation Error Recognition Algorithm Based on Particle Swarm Optimization neural network

Optimization Design of English Translation Error Recognition...

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2023 International conference on Power, Electrical Engineering, Electronics and Control, PEEEC 2023

作者： Gao, Fei Wenhua College Faculty of Foreign Languages Hubei Wuhan430000 China

ISBN: (纸本)9798350329124

Nowadays, there are a large number of bilingual translated texts on the Internet. It is a crucial problem to build a practical bilingual corpus through the processing of translated texts. Based on NN(neural network) encoder, the source language sequence with start and end marks is input, which is converted into distributed word vector data, and then transmitted to NN, including the background vector of source language information, which is input to decoder NN, and the target language sequence is calculated and input with NN as the carrier. This directly leads to the fact that when English translation robots translate English audio, it is difficult to get the same English audio data as the original text, thus reducing translation efficiency or causing translation errors. Improve the ability to eliminate translation errors. In this paper, PSO (particle swarm optimization) algorithm is put forward to optimize it. Simulation results show that the recognition speed of this algorithm is relatively high, and PSO algorithm is used to automatically optimize it, which realizes the elimination of English translation errors. Finally, the experimental results are tested and analyzed, and a conclusion is drawn, which verifies the feasibility of this algorithm. © 2023 IEEE.

关键词： Errors

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Solving Distribute Flexible Flow Shop Scheduling Problem with an Imitation Learning Framework 42

Solving Distribute Flexible Flow Shop Scheduling Problem wit...

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42nd Chinese Control conference, CCC 2023

作者： Ma, Waner Qu, Qingyu Liu, Kexin Beihang University Automation Science and Electrical Engineering Beijing100191 China

ISBN: (纸本)9789887581543

This paper studies the distributed flexible flow shop scheduling problem (DFFSP), where the transportation time between different factories needs to be considered and each machine has a different startup time. A mixed integer linear programming (MILP) model of DFFSP is proposed, and a smart algorithm based on imitation learning for branch-and-bound (B&B) is used to find the scheduling plan that minimizes the total processing time. The graph convolutional neural network model is trained using imitation learning from strong branch expert rules. Finally, we demonstrate the efficiency of our algorithm with simulation experiments. The results indicate that our algorithm demonstrates the most efficient search performance with respect to both the number of nodes explored and search time compared to the four traditional B&B strategies. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： neural network models

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