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HaloFL: Efficient Heterogeneity-Aware Federated Learning Through Optimal Submodel Extraction and Dynamic Sparse Adjustment

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Lian, Zirui Cao, Qianyue Liang, Chao Cao, Jing Zhu, Zongwei Yang, Zhi Ji, Cheng Li, Changlong Zhou, Xuehai School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China School of Software Engineering University of Science and Technology of China Hefei230026 China Xi'an Modern Control Technology Research Institute China Dfh satellite co. Ltd Cast China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Technology East China Normal University Shanghai200063 China

Federated Learning (FL) is an advanced framework that enables collaborative training of machine learning models across edge devices. An effective strategy to enhance training efficiency is to allocate the optimal submodel based on each device's resource capabilities. However, system heterogeneity significantly increases the difficulty of allocating submodel parameter budgets appropriately for each device, leading to the straggler problem. Meanwhile, data heterogeneity complicates the selection of the optimal submodel structure for specific devices, thereby impacting training performance. Furthermore, the dynamic nature of edge environments, such as fluctuations in network communication and computational resources, exacerbates these challenges, making it even more difficult to precisely extract appropriately sized and structured submodels from the global model. To address the challenges in heterogeneous training environments, we propose an efficient FL framework, namely HaloFL. The framework dynamically adjusts the structure and parameter budget of submodels during training by evaluating three dimensions: model-wise performance, layer-wise performance, and unit-wise performance. First, we design a data-aware model unit importance evaluation method to determine the optimal submodel structure for different data distributions. Next, using this evaluation method, we analyze the importance of model layers and reallocate parameters from non-critical layers to critical layers within a fixed parameter budget, further optimizing the submodel structure. Finally, we introduce a resource-aware dual-UCB multi-armed bandit agent, which dynamically adjusts the total parameter budget of submodels according to changes in the training environment, allowing the framework to better adapt to the performance differences of heterogeneous devices. Experimental results demonstrate that HaloFL exhibits outstanding efficiency in various dynamic and heterogeneous scenarios, achieving up to a 14

关键词： Federated learning

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Analysis of Acoustic Emission Signals from SiC MOSFETs Using Robust Local Mean Decomposition and Generalized S-Transform

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhang, Wenbiao Lei, Yongjiang Kong, Fanchen Zhang, Guoqiang North China Electric Power University School of Control and Computer Engineering Beijing102206 China Power Transmission and Transformation Engineering Technology Research Institute State Grid Electric Power Engineering Research Institute Beijing100055 China

Power modules are the most vulnerable components in power electronic systems. It is essential to conduct fault diagnosis and implement protections on power modules to prevent further damage to the system. Existing methods primarily identify faults in power modules through electrical, magnetic, and junction temperature parameters. However, research on the application of acoustic emission (AE) method to monitor power modules is still limited. This article proposes a scheme that leverages the intrinsic relationship between AE signals and the switching states of SiC MOSFETs. The robust local mean decomposition (RLMD) and the generalized S-transform (GST) are introduced. The feasibility and superiority of these methods are initially validated through numerical simulations. Experimental investigations are conducted by a double pulse platform for SiC MOSFETs. The model between time-frequency characteristics of AE signals and external electrical parameters is established under normal conditions. By comparing the Rényi entropy of AE signals from SiC MOSFETs under normal and faulty conditions, which differ by approximately three times, it is demonstrated that Rényi entropy can be used to identify faults. © 1963-2012 IEEE.

关键词： Acoustic emission testing

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SELM: From Efficient Autonomous Exploration to Long-term Monitoring in Semantic Level

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Lang, Fang Qin, Yongsen Wang, Yinchuan Liu, Jin Wang, Chaoqun Song, Wei Zhu, Qiuguo Song, Rui Zhejiang University Polytechnic Institute Hangzhou310015 China Shandong University School of Control Science and Engineering Jinan China Zhejiang University Robotics Institute Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China

Maintaining up-to-date environmental models from initial deployment through long-term autonomy in service is critical for applications like navigation and task planning. To address the challenges of persistent monitoring in unknown environments, we introduce a Two-Stage monitoring strategy, termed the Semantic-level autonomous Exploration and Long-term environment Monitoring (SELM) framework. In the first stage, we introduce a novel semantic exploration method to adapt to new environments quickly. Leveraging the semantic information within the incrementally constructed 3D Scene Graph (3DSG), we combine the Next-Best-View (NBV) selection with room semantics, introducing a more efficient and comprehensive approach for multi-room indoor environment exploration. In addition, the exploration provides patrol routes, the room distance-connectivity graph, and complete environment initial states for subsequential monitoring. The monitoring stage aims to persistently patrol to update the world model in the presence of dynamic changes, including changes in objects' positions. We formulate the long-term monitoring problem as the Partially Observable Markov Decision Process (POMDP) to cope with the environmental uncertainty. To solve the POMDP, we propose the Graph Attention Bidirectional long short-term memory Proximal Policy Optimization (GABPPO) algorithm for the optimal patrol strategy. The feasibility and effectiveness of the proposed SELM framework are verified through extensive experiments. © 2016 IEEE.

关键词： Environmental monitoring

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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & Informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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Data-Driven vs Traditional Approaches to Power Transformer’s Top-Oil Temperature Estimation

arXiv

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arXiv 2025年

作者： Tembo, Francis Bragone, Federica Laneryd, Tor Barreau, Matthieu Morozovska, Kateryna Division of Decision and Control Systems Department of Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Division of Computational Science and Technology Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden Hitachi Energy Research Västerås Sweden

Power transformers are subjected to electrical currents and temperature fluctuations that, if not properly controlled, can lead to major deterioration of their insulation system. Therefore, monitoring the temperature of a power transformer is fundamental to ensure a long-term operational life. Models presented in the IEC 60076-7 and IEEE standards, for example, monitor the temperature by calculating the top-oil and the hot-spot temperatures. However, these models are not very accurate and rely on the power transformers’ properties. This paper focuses on finding an alternative method to predict the top-oil temperatures given previous measurements. Given the large quantities of data available, machine learning methods for time series forecasting are analyzed and compared to the real measurements and the corresponding prediction of the IEC standard. The methods tested are Artificial Neural Networks (ANNs), Time-series Dense Encoder (TiDE), and Temporal Convolutional Networks (TCN) using different combinations of historical measurements. Each of these methods outperformed the IEC 60076-7 model and they are extended to estimate the temperature rise over ambient. To enhance prediction reliability, we explore the application of quantile regression to construct prediction intervals for the expected top-oil temperature ranges. The best-performing model successfully estimates conditional quantiles that provide sufficient coverage. Copyright © 2025, The Authors. All rights reserved.

关键词： Distribution transformers

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Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks

arXiv

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arXiv 2025年

作者： Li, Sirui Bragone, Federica Barreau, Matthieu Laneryd, Tor Morozovska, Kateryna Division of Decision and Control Systems Department of Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Division of Computational Science and Technology Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden Hitachi Energy Research Västerås Sweden

Our work aims at simulating and predicting the temperature conditions inside a power transformer using Physics-Informed Neural Networks (PINNs). The predictions obtained are then used to determine the optimal placement for temperature sensors inside the transformer under the constraint of a limited number of sensors, enabling efficient performance monitoring. The method consists of combining PINNs with Mixed Integer Optimization Programming to obtain the optimal temperature reconstruction inside the transformer. First, we extend our PINN model for thermal modelling of power transformers to solve the heat diffusion equation from 1D to 2D space. Finally, we construct an optimal sensor placement model inside the transformer that can be applied to problems in 1D and 2D. Copyright © 2025, The Authors. All rights reserved.

关键词： Integer programming

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MKMGCN-DDI: Predicting Drug-Drug Interactions via Magnetic Graph Convolutional Network With Multiple Kernels

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 469-480页

作者： Ming Chen Yunhan Pan Xiujuan Lei Chunyan Ji Yinglong Dai Yi Pan Department of Artificial Intelligence Hunan Normal University Changsha Hunan China School of Computer Science Shaanxi Normal University Xi’an China Computer Science Department BNU-HKBU United International College Zhuhai China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have demonstrated encouraging performance in predicting DDIs. However, most studies overlook the comprehensive aspects of DDIs, such as the coexistence of types of pharmacological changes and the asymmetric roles of drugs. In this article, we define new prediction tasks, taking into account both enhancive or depressive changes and the roles of drugs, and then establish spectral GNNs to predict comprehensive information of DDIs. First, we formally define several tasks, including joint prediction tasks designed to leverage both types and directions. These tasks deduce to sub-tasks in previous studies. Then, we propose a unified framework, the MKMGCN-DDI, via introducing two Magnetic Laplacian matrices to encode comprehension information within DDIs, defining multiple graph filters, and designing multiple-kernel based Magnetic graph convolutional networks (MKMGCN). Experiments across three datasets show that it not only has good adaptability to multiple tasks but also significantly improves results on simple tasks. Case studies on breast neoplasms and lung neoplasms verify its feasibility, as over half of top-10 items are supported.

关键词： Drugs Predictive models Magnetic separation Laplace equations Bioinformatics Sodium Market research Graph convolutional networks computer science Computational modeling

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Mixed Small Gain and Phase Theorem: A new view using Scale Relative Graphs

arXiv

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arXiv 2025年

作者： Baron-Prada, Eder Anta, Adolfo Padoan, Alberto Dörfler, Florian Austrian Institute of Technology Vienna1210 Austria Automatic Control Laboratory ETH Zurich Zürich8092 Switzerland Austrian Institute of Technology Vienna1210 Austria Department of Electrical & Computer Engineering University of British Columbia Canada Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland

We introduce a novel approach to feedback stability analysis for linear time-invariant (LTI) systems, overcoming the limitations of the sectoriality assumption in the small phase theorem. While phase analysis for single-input single-output (SISO) systems is well-established, multi-input multi-output (MIMO) systems lack a comprehensive phase analysis until recent advances introduced with the small-phase theorem. A limitation of the small-phase theorem is the sectorial condition, which states that an operator’s eigenvalues must lie within a specified angle sector of the complex plane. We propose a framework based on Scaled Relative Graphs (SRGs) to remove this assumption. We derive two main results: a graphical set-based stability condition using SRGs and a small-phase theorem with no sectorial assumption. These results broaden the scope of phase analysis and feedback stability for MIMO systems. © 2025, CC BY.

关键词： MIMO systems

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The effect of PV modules on temperature conditions of nearby power cables

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Frontiers in Energy Research 2025年 13卷

作者： Czapp, Stanislaw Szultka, Seweryn Tomaszewski, Adam Khan, Laiq Faculty of Electrical and Control Engineering Gdansk University of Technology Gdansk Poland Institute of Fluid-Flow Machinery Polish Academy of Sciences Gdansk Poland Department of Electrical and Computer Engineering COMSATS University Islamabad Pakistan

The highest permissible load of power cables (ampacity) depends strongly on the ambient temperature, which in the case of cables surrounded by free air is generally assumed to be 25°C. Considering the installation of photovoltaic (PV) modules, the thermal conditions for the cables connected to these modules may be very unfavorable. The cables are laid under the module and the space underneath it is heated by this module. The aim of the paper is to present a numerical model of an example PV module mounted on the roof of a building, as well as the results of numerical calculations of the temperature around the module. Based on the numerical model of the PV system and advanced calculations it is possible to introduce a correction factor to determine the ampacity of the power cables located in the vicinity of the PV module. The authors’ research allows for the correct determination of the cable ampacity, avoiding its overheating and preventing the risk of fire. Copyright © 2025 Czapp, Szultka, Tomaszewski and Khan.

关键词： ampacity energy photovoltaic source power cable power system thermal effect

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QoI-Aware Configuration Adaptation and Heterogeneous Resource Allocation for Edge Video Analytics

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IEEE Internet of Things Journal 2025年

作者： He, Zixuan Sun, Yanjing Zhang, Beibei Dong, Kaiwen Wang, Bowen Xu, Hongli China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China China University of Mining and Technology IoT Perception Mine Research Center Xuzhou221116 China School of Information and Control Engineering China University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China University of Science and Technology of China Suzhou Institute for Advanced Research Jiangsu Suzhou215123 China

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive Deep Neural Network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this paper, we propose an edge-enabled multi-video analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov Decision Process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion deep reinforcement learning based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes. © 2025 IEEE.

关键词： Markov processes

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