:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Matsutani, Hiroki, Matsuda, Naoki, Sugiura, Naoto
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Distributed, Parallel, and Cluster Computing
Acceso en línea:	https://arxiv.org/abs/2602.22812
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices
por: Yao, Dezhong, et al.
Publicado: (2025)

10Cache: Heterogeneous Resource-Aware Tensor Caching and Migration for LLM Training
por: Afroz, Sabiha, et al.
Publicado: (2025)

HACK: Homomorphic Acceleration via Compression of the Key-Value Cache for Disaggregated LLM Inference
por: Zhang, Zeyu, et al.
Publicado: (2025)

SlimCaching: Edge Caching of Mixture-of-Experts for Distributed Inference
por: Chen, Qian, et al.
Publicado: (2025)

Efficient Unified Caching for Accelerating Heterogeneous AI Workloads
por: Wang, Tianze, et al.
Publicado: (2025)

Accelerating Transposed Convolutions on FPGA-based Edge Devices
por: Haris, Jude, et al.
Publicado: (2025)

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices
por: Shen, Tao, et al.
Publicado: (2025)

Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices
por: Pfeiffer, Kilian, et al.
Publicado: (2024)

Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services
por: Liu, Zhang, et al.
Publicado: (2024)

REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments
por: Desai, Humaid Ahmed, et al.
Publicado: (2023)

Adaptive Stream Processing on Edge Devices through Active Inference
por: Sedlak, Boris, et al.
Publicado: (2024)

Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication
por: Ochiai, Hideya, et al.
Publicado: (2024)

Accelerating Depthwise Separable Convolutions on Ultra-Low-Power Devices
por: Daghero, Francesco, et al.
Publicado: (2024)

Prada: Black-Box LLM Adaptation with Private Data on Resource-Constrained Devices
por: Wang, Ziyao, et al.
Publicado: (2025)

Boosting Resource-Constrained Federated Learning Systems with Guessed Updates
por: Boukhari, Mohamed Yassine, et al.
Publicado: (2021)

Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices
por: Feng, Chao, et al.
Publicado: (2025)

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices
por: Molina, Romina Soledad, et al.
Publicado: (2025)

Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization
por: Guo, Kun, et al.
Publicado: (2025)

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks
por: Xu, Changfu, et al.
Publicado: (2024)

Learn More by Using Less: Distributed Learning with Energy-Constrained Devices
por: Pereira, Roberto, et al.
Publicado: (2024)

FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
por: Liu, Tao, et al.
Publicado: (2026)

Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks
por: Yu, Lu, et al.
Publicado: (2024)

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
por: Mujtaba, Ahmed, et al.
Publicado: (2025)

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM
por: Gu, Shunxian, et al.
Publicado: (2025)

Priority-Aware Model-Distributed Inference at Edge Networks
por: Li, Teng, et al.
Publicado: (2024)

AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
por: Jia, Chentao, et al.
Publicado: (2023)

LIME:Accelerating Collaborative Lossless LLM Inference on Memory-Constrained Edge Devices
por: Sun, Mingyu, et al.
Publicado: (2025)

Not All Tokens Are Worth Caching: Learning Semantic-Aware Eviction for LLM Prefix Caches
por: Fang, Shaoke, et al.
Publicado: (2026)

TDC-Cache: A Trustworthy Decentralized Cooperative Caching Framework for Web3.0
por: Chen, Jinyu, et al.
Publicado: (2025)

MultiTASC++: A Continuously Adaptive Scheduler for Edge-Based Multi-Device Cascade Inference
por: Nikolaidis, Sokratis, et al.
Publicado: (2024)

Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks
por: Atapour, S. Kawa, et al.
Publicado: (2024)

EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting
por: Yu, Zhongzhi, et al.
Publicado: (2024)

Preble: Efficient Distributed Prompt Scheduling for LLM Serving
por: Srivatsa, Vikranth, et al.
Publicado: (2024)

Intelligent Orchestration of Distributed Large Foundation Model Inference at the Edge
por: Koch, Fernando, et al.
Publicado: (2025)

Distributed Convolutional Neural Network Training on Mobile and Edge Clusters
por: Rama, Pranav, et al.
Publicado: (2024)

Improved Decision Module Selection for Hierarchical Inference in Resource-Constrained Edge Devices
por: Behera, Adarsh Prasad, et al.
Publicado: (2024)

Incentivizing Permissionless Distributed Learning of LLMs
por: Lidin, Joel, et al.
Publicado: (2025)

LLMBridge: Reducing Costs to Access LLMs in a Prompt-Centric Internet
por: Martin, Noah, et al.
Publicado: (2024)

Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack
por: Yang, Qiantao, et al.
Publicado: (2026)

PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints
por: Guo, Yuanchun, et al.
Publicado: (2025)