主题图谱 / distributed parallel and cluster computing cs.dc

distributed parallel and cluster computing cs.dc

GeneTech 知识引擎共收录 120 篇相关科研实体，横跨 15 个垂直领域。数据来自 OpenAlex、arXiv、Crossref、PubMed、Europe PMC 等公开学术源，全部可溯源到原文。

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代表性文献（按质量分排序，前 60 条）

• RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models
  2026 · 具身智能 · Vision-Language-Action (VLA) models are mainstream in embodied intelligence but face high inference costs. Edge-Cloud Collaborative (ECC) deployment offers an effective fix by easing edge-device computing pressure to mee…
• Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences
  2026 · 生命科学 · Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research under strict privacy regulation. The methods are maturing fast, but t…
• Exploiting network topology in brain-scale simulations of spiking neural networks
  2026 · 类脑计算 · Simulation code for conventional supercomputers serves as a reference for neuromorphic computing systems. The present bottleneck of distributed large-scale spiking neuronal network simulations is the communication betwee…
• Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences
  2026 · 隐私计算与数据要素 · Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research under strict privacy regulation. The methods are maturing fast, but t…
• Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learning
  2026 · 隐私计算与数据要素 · Local Energy Communities are emerging as crucial players in the landscape of sustainable development. A significant challenge for these communities is achieving self-sufficiency through effective management of the balanc…
• SoK: A Systematic Bidirectional Literature Review of AI & DLT Convergence
  2026 · 隐私计算与数据要素 · The integration of Artificial Intelligence (AI) with Distributed Ledger Technology (DLT) has become a growing research area, yet contributions tend to cluster around specific application domains or examine only one direc…
• Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning
  2026 · 隐私计算与数据要素 · Modern big-data systems generate massive, heterogeneous, and geographically dispersed streams that are large-scale and privacy-sensitive, making centralization challenging. While federated learning (FL) provides a privac…
• Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
  2026 · 隐私计算与数据要素 · Big data scenarios, where massive, heterogeneous datasets are distributed across clients, demand scalable, privacy-preserving learning methods. Federated learning (FL) enables decentralized training of machine learning (…
• FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systems
  2026 · 隐私计算与数据要素 · Privacy-Preserving Federated Learning (PPFL) is a Decentralized machine learning paradigm that enables multiple participants to collaboratively train a global model without sharing their data with the integration of cryp…
• Secure and Privacy-Preserving Vertical Federated Learning
  2026 · 隐私计算与数据要素 · We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, for the vertically split scenario in feder…
• Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data
  2026 · 隐私计算与数据要素 · Federated Learning (FL) enables distributed training of machine learning models while preserving data privacy. However, FL struggles with heterogeneous, non-IID client data distributions, resulting in sub-optimal and bia…
• ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal
  2026 · 隐私计算与数据要素 · Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance. However, cloud coverage often obscures the Earth's surfa…
• Model-Consistent Byzantine-Resilient Decentralized Federated Learning for Collaborative Missions
  2026 · 隐私计算与数据要素 · Decentralized federated learning (DFL) is a promising paradigm for autonomous nodes to collaboratively train AI models without relying on a central server. However, existing DFL solutions do not guarantee global model co…
• Pre-Deployment Complexity Estimation for Federated Perception Systems
  2026 · 隐私计算与数据要素 · Edge AI systems increasingly rely on federated learning to train perception models in distributed, privacy-preserving, and resource-constrained environments. Before training, however, practitioners often lack practical t…
• Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions
  2026 · 隐私计算与数据要素 · Large language models (LLMs) have become core components of cloud-based intelligent services in academia and industry, yet their training and deployment are hindered by high computational costs, data centralization, and …
• Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification
  2026 · 隐私计算与数据要素 · Early detection of plant diseases is critical for improving crop productivity, while it also facilitates the foundations of precision agriculture. Recent advances in distributed deep learning have enabled plant disease c…
• Latency-Optimal Adaptive Split Inference for Privacy-Preserving Cloud-Edge-End Collaboration
  2026 · 隐私计算与数据要素 · Internet of Things (IoT) end devices are increasingly expected to support privacy-sensitive batch inference, yet their limited computational resources often make full local execution of convolutional neural networks impr…
• DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
  2026 · 隐私计算与数据要素 · Federated learning enables collaborative model training across distributed clients, yet vanilla FL exposes client updates to the central server. Secure-aggregation schemes protect privacy against an honest-but-curious se…
• AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
  2026 · 隐私计算与数据要素 · Fully Homomorphic Encryption (FHE) enables privacy-preserving Transformer inference, but long-sequence encrypted Transformers quickly exceed single-GPU memory capacity because encoded weights are already large and encryp…
• No Distributed Quantum Advantage for 3-Coloring Rooted Trees and 2-Coloring Even Cycles
  2026 · 量子计算 · Significant effort has been devoted over the past decade to understanding whether quantum resources can provide advantages in distributed computing, and in particular whether they can help overcome locality constraints i…
• DiffPhD: A Unified Differentiable Solver for Projective Heterogeneous Materials in Elastodynamics with Contact-Rich GPU-Acceleration
  2026 · 机器人零部件 · Differentiable simulation of soft bodies is a foundation for system identification, trajectory optimization, and Real2Sim transfer. Yet, existing methods such as the differentiable Projective Dynamics (DiffPD) struggle w…
• Analog computation with transcriptional networks
  2025 · 生物计算 · Transcriptional networks represent one of the most extensively studied types of systems in synthetic biology. Although the completeness of transcriptional networks for digital logic is well-established, *analog* computat…
• CFTel: A Practical Architecture for Robust and Scalable Telerobotics with Cloud-Fog Automation
  2025 · 具身智能 · Telerobotics is a key foundation in autonomous Industrial Cyber-Physical Systems (ICPS), enabling remote operations across various domains. However, conventional cloud-based telerobotics suffers from latency, reliability…
• AVERY: Intent-Driven Adaptive VLM Split Computing via Embodied Self-Awareness for Efficient Disaster Response Systems
  2025 · 具身智能 · Unmanned Aerial Vehicles (UAVs) in disaster response require complex, queryable intelligence that onboard CNNs cannot provide. While Vision-Language Models (VLMs) offer this semantic reasoning, their high resource demand…
• An optimization framework for task allocation in the edge/hub/cloud paradigm
  2025 · 低空经济 · With the advent of the Internet of Things (IoT), novel critical applications have emerged that leverage the edge/hub/cloud paradigm, which diverges from the conventional edge computing perspective. A growing number of su…
• UAV Marketplace Simulation Tool for BVLOS Operations
  2025 · 低空经济 · We present a simulation tool for evaluating team formation in autonomous multi-UAV (Unmanned Aerial Vehicle) missions that operate Beyond Visual Line of Sight (BVLOS). The tool models UAV collaboration and mission execut…
• Constructive community race: full-density spiking neural network model drives neuromorphic computing
  2025 · 类脑计算 · The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neuron…
• Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors
  2025 · 隐私计算与数据要素 · Human Activity Recognition (HAR) benefits various application domains, including health and elderly care. Traditional HAR involves constructing pipelines reliant on centralized user data, which can pose privacy concerns …
• Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers
  2025 · 隐私计算与数据要素 · The integration of IoT and AI has unlocked innovation across industries, but growing privacy concerns and data isolation hinder progress. Traditional centralized ML struggles to overcome these challenges, which has led t…
• Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
  2025 · 隐私计算与数据要素 · Federated distillation has emerged as a promising collaborative machine learning approach, offering enhanced privacy protection and reduced communication compared to traditional federated learning by exchanging model out…
• POPri: Private Federated Learning using Preference-Optimized Synthetic Data
  2025 · 隐私计算与数据要素 · In practical settings, differentially private Federated learning (DP-FL) is the dominant method for training models from private, on-device client data. Recent work has suggested that DP-FL may be enhanced or outperforme…
• Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform
  2025 · 隐私计算与数据要素 · Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly …
• Controlled privacy leakage propagation throughout overlapping grouped learning
  2025 · 隐私计算与数据要素 · Federated Learning (FL) is the standard protocol for collaborative learning. In FL, multiple workers jointly train a shared model. They exchange model updates calculated on their data, while keeping the raw data itself l…
• Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation
  2025 · 隐私计算与数据要素 · Employing self-supervised learning (SSL) methodologies assumes par-amount significance in handling unlabeled polyp datasets when building deep learning-based automatic polyp segmentation models. However, the intricate pr…
• Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning
  2025 · 隐私计算与数据要素 · We propose Fed-DPRoC, a novel federated learning framework designed to jointly provide differential privacy (DP), Byzantine robustness, and communication efficiency. Central to our approach is the concept of robust-compa…
• Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments
  2025 · 隐私计算与数据要素 · Federated Learning (FL) is a decentralized machine learning paradigm that enables clients to collaboratively train models while preserving data privacy. However, the coexistence of model and data heterogeneity gives rise…
• Overcoming Bottlenecks in Homomorphic Encryption for the 2024 Mexican Federal Election
  2025 · 隐私计算与数据要素 · On June 2, 2024, Mexico held its federal elections. The majority of Mexican citizens voted in person at the polls in this historic election. For the first time though, Mexican citizens living outside their country were a…
• MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems
  2025 · 隐私计算与数据要素 · The rapid expansion of immersive Metaverse applications introduces complex challenges at the intersection of performance, privacy, and environmental sustainability. Centralized architectures fall short in addressing thes…
• SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA
  2025 · 隐私计算与数据要素 · Federated fine-tuning is critical for improving the performance of large language models (LLMs) in handling domain-specific tasks while keeping training data decentralized and private. However, prior work has shown that …
• Quantum Federated Learning: Architectural Elements and Future Directions
  2025 · 隐私计算与数据要素 · Federated learning (FL) focuses on collaborative model training without the need to move the private data silos to a central server. Despite its several benefits, the classical FL is plagued with several limitations, suc…
• Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment
  2025 · 隐私计算与数据要素 · This paper proposes a Trans-XFed architecture that combines federated learning with explainable AI techniques for supply chain credit assessment. The proposed model aims to address several key challenges, including priva…
• Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI
  2025 · 隐私计算与数据要素 · Federated Learning (FL) enables collaborative model training while preserving data privacy; however, balancing privacy preservation (PP) and fairness poses significant challenges. In this paper, we present the first unif…
• Stannic: Systolic STochAstic ONliNe SchedulIng AcCelerator
  2025 · 空间计算 · Efficient workload scheduling is a critical challenge in modern heterogeneous computing environments, particularly in high-performance computing (HPC) systems. Traditional software-based schedulers struggle to efficientl…
• Spatio-Temporal Parallelism for Diffusion Model Inference on Heterogeneous Multi-GPU Systems
  2025 · 空间计算 · The widespread adoption of diffusion models for image generation necessitates efficient parallel inference to manage their substantial computational overhead. However, current parallel inference paradigms primarily targe…
• Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks
  2024 · 边缘智能 · Artificial Intelligence Generated Content (AIGC) has gained significant popularity for creating diverse content. Current AIGC models primarily focus on content quality within a centralized framework, resulting in a high …
• Federated Continual Learning for Edge-AI: A Comprehensive Survey
  2024 · 边缘智能 · Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated co…
• Enhanced FIWARE-Based Architecture for Cyberphysical Systems With Tiny Machine Learning and Machine Learning Operations: A Case Study on Urban Mobility Systems
  2024 · 边缘智能 · The rise of AI and the Internet of Things is accelerating the digital transformation of society. Mobility computing presents specific barriers due to its real-time requirements, decentralization, and connectivity through…
• Enhancing Predictive Maintenance in Mining Mobile Machinery through a TinyML-enabled Hierarchical Inference Network
  2024 · 边缘智能 · Mining machinery operating in variable environments faces high wear and unpredictable stress, challenging Predictive Maintenance (PdM). This paper introduces the Edge Sensor Network for Predictive Maintenance (ESN-PdM), …
• MATCH: Model-Aware TVM-based Compilation for Heterogeneous Edge Devices
  2024 · 边缘智能 · Streamlining the deployment of Deep Neural Networks (DNNs) on heterogeneous edge platforms, coupling within the same micro-controller unit (MCU) instruction processors and hardware accelerators for tensor computations, i…
• HTVM: Efficient Neural Network Deployment On Heterogeneous TinyML Platforms
  2024 · 边缘智能 · Optimal deployment of deep neural networks (DNNs) on state-of-the-art Systems-on-Chips (SoCs) is crucial for tiny machine learning (TinyML) at the edge. The complexity of these SoCs makes deployment non-trivial, as they …
• On-device Online Learning and Semantic Management of TinyML Systems
  2024 · 边缘智能 · Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation pre…
• Understanding Large-Scale Plasma Simulation Challenges for Fusion Energy on Supercomputers
  2024 · 核能 · Understanding plasma instabilities is essential for achieving sustainable fusion energy, with large-scale plasma simulations playing a crucial role in both the design and development of next-generation fusion energy devi…
• A Survey on Efficient Federated Learning Methods for Foundation Model Training
  2024 · 隐私计算与数据要素 · Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. However, new approaches to FL often discuss their contributions involving…
• Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
  2024 · 隐私计算与数据要素 · Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, thereby reducing privacy risks. However, …
• Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
  2024 · 隐私计算与数据要素 · In traditional federated learning, a single global model cannot perform equally well for all clients. Therefore, the need to achieve the client-level fairness in federated system has been emphasized, which can be realize…
• Tackling Selfish Clients in Federated Learning
  2024 · 隐私计算与数据要素 · Federated Learning (FL) is a distributed machine learning paradigm facilitating participants to collaboratively train a model without revealing their local data. However, when FL is deployed into the wild, some intellige…
• FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
  2024 · 隐私计算与数据要素 · Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Traditional approaches, including heuristi…
• FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
  2024 · 隐私计算与数据要素 · Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data. With the rise of deep learning, large-scale models have garnered sign…
• Workflow Optimization for Parallel Split Learning
  2024 · 隐私计算与数据要素 · Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to train multi-parameter neural networks (NNs) and participate in federated learning (FL). In a nutshell, SL splits the NN mo…
• TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
  2024 · 隐私计算与数据要素 · Federated learning is a distributed collaborative machine learning paradigm that has gained strong momentum in recent years. In federated learning, a central server periodically coordinates models with clients and aggreg…

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