Frontiers of Information Technology & Electronic Engineering最新文献_第4页

Federated learning on non-IID and long-tailed data via dual-decoupling 通过双解耦对非 IID 和长尾数据进行联合学习

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-06-07 DOI: 10.1631/fitee.2300284

Zhaohui Wang, Hongjiao Li, Jinguo Li, Renhao Hu, Baojin Wang

{"title":"Federated learning on non-IID and long-tailed data via dual-decoupling","authors":"Zhaohui Wang, Hongjiao Li, Jinguo Li, Renhao Hu, Baojin Wang","doi":"10.1631/fitee.2300284","DOIUrl":"https://doi.org/10.1631/fitee.2300284","url":null,"abstract":"Federated learning (FL), a cutting-edge distributed machine learning training paradigm, aims to generate a global model by collaborating on the training of client models without revealing local private data. The cooccurrence of non-independent and identically distributed (non-IID) and long-tailed distribution in FL is one challenge that substantially degrades aggregate performance. In this paper, we present a corresponding solution called federated dual-decoupling via model and logit calibration (FedDDC) for non-IID and long-tailed distributions. The model is characterized by three aspects. First, we decouple the global model into the feature extractor and the classifier to fine-tune the components affected by the joint problem. For the biased feature extractor, we propose a client confidence re-weighting scheme to assist calibration, which assigns optimal weights to each client. For the biased classifier, we apply the classifier re-balancing method for fine-tuning. Then, we calibrate and integrate the client confidence re-weighted logits with the re-balanced logits to obtain the unbiased logits. Finally, we use decoupled knowledge distillation for the first time in the joint problem to enhance the accuracy of the global model by extracting the knowledge of the unbiased model. Numerous experiments demonstrate that on non-IID and long-tailed data in FL, our approach outperforms state-of-the-art methods.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"52 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Target parameter estimation for OTFS-integrated radar and communications based on sparse reconstruction preprocessing 基于稀疏重构预处理的 OTFS 集成雷达和通信的目标参数估计

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-06-07 DOI: 10.1631/fitee.2300462

Zhenkai Zhang, Xiaoke Shang, Yue Xiao

引用次数: 0

Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks 将图神经网络与深度强化学习相结合，促进计算力网络的资源分配

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-06-07 DOI: 10.1631/fitee.2300009

Xueying Han, Mingxi Xie, Ke Yu, Xiaohong Huang, Zongpeng Du, Huijuan Yao

引用次数: 0

A survey of energy-efficient strategies for federated learning inmobile edge computing 移动边缘计算联合学习的节能策略调查

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-06-07 DOI: 10.1631/fitee.2300181

Kang Yan, Nina Shu, Tao Wu, Chunsheng Liu, Panlong Yang

{"title":"A survey of energy-efficient strategies for federated learning inmobile edge computing","authors":"Kang Yan, Nina Shu, Tao Wu, Chunsheng Liu, Panlong Yang","doi":"10.1631/fitee.2300181","DOIUrl":"https://doi.org/10.1631/fitee.2300181","url":null,"abstract":"With the booming development of fifth-generation network technology and Internet of Things, the number of end-user devices (EDs) and diverse applications is surging, resulting in massive data generated at the edge of networks. To process these data efficiently, the innovative mobile edge computing (MEC) framework has emerged to guarantee low latency and enable efficient computing close to the user traffic. Recently, federated learning (FL) has demonstrated its empirical success in edge computing due to its privacy-preserving advantages. Thus, it becomes a promising solution for analyzing and processing distributed data on EDs in various machine learning tasks, which are the major workloads in MEC. Unfortunately, EDs are typically powered by batteries with limited capacity, which brings challenges when performing energy-intensive FL tasks. To address these challenges, many strategies have been proposed to save energy in FL. Considering the absence of a survey that thoroughly summarizes and classifies these strategies, in this paper, we provide a comprehensive survey of recent advances in energy-efficient strategies for FL in MEC. Specifically, we first introduce the system model and energy consumption models in FL, in terms of computation and communication. Then we analyze the challenges regarding improving energy efficiency and summarize the energy-efficient strategies from three perspectives: learning-based, resource allocation, and client selection. We conduct a detailed analysis of these strategies, comparing their advantages and disadvantages. Additionally, we visually illustrate the impact of these strategies on the performance of FL by showcasing experimental results. Finally, several potential future research directions for energy-efficient FL are discussed.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"9 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-exit self-distillation with appropriate teachers 与合适的教师进行多出口自馏

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-05-10 DOI: 10.1631/fitee.2200644

Wujie Sun, Defang Chen, Can Wang, Deshi Ye, Yan Feng, Chun Chen

{"title":"Multi-exit self-distillation with appropriate teachers","authors":"Wujie Sun, Defang Chen, Can Wang, Deshi Ye, Yan Feng, Chun Chen","doi":"10.1631/fitee.2200644","DOIUrl":"https://doi.org/10.1631/fitee.2200644","url":null,"abstract":"Multi-exit architecture allows early-stop inference to reduce computational cost, which can be used in resource-constrained circumstances. Recent works combine the multi-exit architecture with self-distillation to simultaneously achieve high efficiency and decent performance at different network depths. However, existing methods mainly transfer knowledge from deep exits or a single ensemble to guide all exits, without considering that inappropriate learning gaps between students and teachers may degrade the model performance, especially in shallow exits. To address this issue, we propose Multi-exit self-distillation with Appropriate TEachers (MATE) to provide diverse and appropriate teacher knowledge for each exit. In MATE, multiple ensemble teachers are obtained from all exits with different trainable weights. Each exit subsequently receives knowledge from all teachers, while focusing mainly on its primary teacher to keep an appropriate gap for efficient knowledge transfer. In this way, MATE achieves diversity in knowledge distillation while ensuring learning efficiency. Experimental results on CIFAR-100, TinyImageNet, and three fine-grained datasets demonstrate that MATE consistently outperforms state-of-the-art multi-exit self-distillation methods with various network architectures.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"7 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and verification of an FPGA programmable logic element based on Sense-Switch pFLASH 基于感应开关 pFLASH 的 FPGA 可编程逻辑元件的设计与验证

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-05-10 DOI: 10.1631/fitee.2300454

Zhengzhou Cao, Guozhu Liu, Yanfei Zhang, Yueer Shan, Yuting Xu

{"title":"Design and verification of an FPGA programmable logic element based on Sense-Switch pFLASH","authors":"Zhengzhou Cao, Guozhu Liu, Yanfei Zhang, Yueer Shan, Yuting Xu","doi":"10.1631/fitee.2300454","DOIUrl":"https://doi.org/10.1631/fitee.2300454","url":null,"abstract":"This paper proposes a kind of programmable logic element (PLE) based on Sense-Switch pFLASH technology. By programming Sense-Switch pFLASH, all three-bit look-up table (LUT3) functions, partial four-bit look-up table (LUT4) functions, latch functions, and d flip flop (DFF) with enable and reset functions can be realized. Because PLE uses a choice of operational logic (COOL) approach for the operation of logic functions, it allows any logic circuit to be implemented at any ratio of combinatorial logic to register. This intrinsic property makes it close to the basic application specific integrated circuit (ASIC) cell in terms of fine granularity, thus allowing ASIC-like cell-based mappers to apply all their optimization potential. By measuring Sense-Switch pFLASH and PLE circuits, the results show that the “on” state driving current of the Sense-Switch pFLASH is about 245.52 µA, and that the “off” state leakage current is about 0.1 pA. The programmable function of PLE works normally. The delay of the typical combinatorial logic operation AND3 is 0.69 ns, and the delay of the sequential logic operation DFF is 0.65 ns, both of which meet the requirements of the design technical index.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"246 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards sustainable adversarial training with successive perturbation generation 利用连续扰动生成实现可持续对抗训练

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-05-10 DOI: 10.1631/fitee.2300474

Wei Lin, Lichuan Liao

{"title":"Towards sustainable adversarial training with successive perturbation generation","authors":"Wei Lin, Lichuan Liao","doi":"10.1631/fitee.2300474","DOIUrl":"https://doi.org/10.1631/fitee.2300474","url":null,"abstract":"Adversarial training with online-generated adversarial examples has achieved promising performance in defending adversarial attacks and improving robustness of convolutional neural network models. However, most existing adversarial training methods are dedicated to finding strong adversarial examples for forcing the model to learn the adversarial data distribution, which inevitably imposes a large computational overhead and results in a decrease in the generalization performance on clean data. In this paper, we show that progressively enhancing the adversarial strength of adversarial examples across training epochs can effectively improve the model robustness, and appropriate model shifting can preserve the generalization performance of models in conjunction with negligible computational cost. To this end, we propose a successive perturbation generation scheme for adversarial training (SPGAT), which progressively strengthens the adversarial examples by adding the perturbations on adversarial examples transferred from the previous epoch and shifts models across the epochs to improve the efficiency of adversarial training. The proposed SPGAT is both efficient and effective; e.g., the computation time of our method is 900 min as against the 4100 min duration observed in the case of standard adversarial training, and the performance boost is more than 7% and 3% in terms of adversarial accuracy and clean accuracy, respectively. We extensively evaluate the SPGAT on various datasets, including small-scale MNIST, middle-scale CIFAR-10, and large-scale CIFAR-100. The experimental results show that our method is more efficient while performing favorably against state-of-the-art methods.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"59 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Securing multi-chain consensus against diverse miner behavior attacks in blockchain networks 确保多链共识免受区块链网络中多样化矿工行为的攻击

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-05-10 DOI: 10.1631/fitee.2200505

Wenbo Zhang, Tao Wang, Chaoyang Zhang, Jingyu Feng

{"title":"Securing multi-chain consensus against diverse miner behavior attacks in blockchain networks","authors":"Wenbo Zhang, Tao Wang, Chaoyang Zhang, Jingyu Feng","doi":"10.1631/fitee.2200505","DOIUrl":"https://doi.org/10.1631/fitee.2200505","url":null,"abstract":"As cross-chain technologies enable interactions among different blockchains (hereinafter “chains”), multi-chain consensus is becoming increasingly important in blockchain networks. However, more attention has been paid to single-chain consensus schemes. Multi-chain consensus schemes with trusted miner participation have not been considered, thus offering opportunities for malicious users to launch diverse miner behavior (DMB) attacks on different chains. DMB attackers can be friendly in the consensus process on some chains, called mask chains, to enhance their trust value, while on others, called kill chains, they engage in destructive behaviors on the network. In this paper, we propose a multi-chain consensus scheme named Proof-of-DiscTrust (PoDT) to defend against DMB attacks. The idea of distinctive trust (DiscTrust) is introduced to evaluate the trust value of each user across different chains. The trustworthiness of a user is split into local and global trust values. A dynamic behavior prediction scheme is designed to enforce DiscTrust to prevent an intensive DMB attacker from maintaining strong trust by alternately creating true or false blocks on the kill chain. Three trusted miner selection algorithms for multi-chain environments can be implemented to select network miners, chain miners, and chain miner leaders, separately. Simulation results show that PoDT is secure against DMB attacks and more effective than traditional consensus schemes in multi-chain environments.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"154 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140926190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An anti-collision algorithm for robotic search-and-rescue tasks in unknown dynamic environments 未知动态环境中机器人搜救任务的防碰撞算法

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-05-10 DOI: 10.1631/fitee.2300151

Yang Chen, Dianxi Shi, Huanhuan Yang, Tongyue Li, Zhen Wang

{"title":"An anti-collision algorithm for robotic search-and-rescue tasks in unknown dynamic environments","authors":"Yang Chen, Dianxi Shi, Huanhuan Yang, Tongyue Li, Zhen Wang","doi":"10.1631/fitee.2300151","DOIUrl":"https://doi.org/10.1631/fitee.2300151","url":null,"abstract":"This paper deals with the search-and-rescue tasks of a mobile robot with multiple interesting targets in an unknown dynamic environment. The problem is challenging because the mobile robot needs to search for multiple targets while avoiding obstacles simultaneously. To ensure that the mobile robot avoids obstacles properly, we propose a mixed-strategy Nash equilibrium based Dyna-Q (MNDQ) algorithm. First, a multi-objective layered structure is introduced to simplify the representation of multiple objectives and reduce computational complexity. This structure divides the overall task into subtasks, including searching for targets and avoiding obstacles. Second, a risk-monitoring mechanism is proposed based on the relative positions of dynamic risks. This mechanism helps the robot avoid potential collisions and unnecessary detours. Then, to improve sampling efficiency, MNDQ is presented, which combines Dyna-Q and mixed-strategy Nash equilibrium. By using mixed-strategy Nash equilibrium, the agent makes decisions in the form of probabilities, maximizing the expected rewards and improving the overall performance of the Dyna-Q algorithm. Furthermore, a series of simulations are conducted to verify the effectiveness of the proposed method. The results show that MNDQ performs well and exhibits robustness, providing a competitive solution for future autonomous robot navigation tasks.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"3 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FaSRnet: a feature and semantics refinement network for human pose estimation FaSRnet：用于人体姿态估计的特征和语义细化网络

IF 3 3区工程技术

Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-05-10 DOI: 10.1631/fitee.2200639

Yuanhong Zhong, Qianfeng Xu, Daidi Zhong, Xun Yang, Shanshan Wang

{"title":"FaSRnet: a feature and semantics refinement network for human pose estimation","authors":"Yuanhong Zhong, Qianfeng Xu, Daidi Zhong, Xun Yang, Shanshan Wang","doi":"10.1631/fitee.2200639","DOIUrl":"https://doi.org/10.1631/fitee.2200639","url":null,"abstract":"Due to factors such as motion blur, video out-of-focus, and occlusion, multi-frame human pose estimation is a challenging task. Exploiting temporal consistency between consecutive frames is an efficient approach for addressing this issue. Currently, most methods explore temporal consistency through refinements of the final heatmaps. The heatmaps contain the semantics information of key points, and can improve the detection quality to a certain extent. However, they are generated by features, and feature-level refinements are rarely considered. In this paper, we propose a human pose estimation framework with refinements at the feature and semantics levels. We align auxiliary features with the features of the current frame to reduce the loss caused by different feature distributions. An attention mechanism is then used to fuse auxiliary features with current features. In terms of semantics, we use the difference information between adjacent heatmaps as auxiliary features to refine the current heatmaps. The method is validated on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018, and the results demonstrate the effectiveness of our method.","PeriodicalId":12608,"journal":{"name":"Frontiers of Information Technology & Electronic Engineering","volume":"13 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0