Imagen de Portada

ACME: Adaptive Customization of Large Models via Distributed Systems

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Dai, Ziming, Qiu, Chao, Gao, Fei, Zhao, Yunfeng, Wang, Xiaofei
Formato:	Preprint
Publicado:	2025
Materias:	Distributed, Parallel, and Cluster Computing Artificial Intelligence C.2.4; I.2.6
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments
por: Zhang, Guilin, et al.
Publicado: (2025)

EWSJF: An Adaptive Scheduler with Hybrid Partitioning for Mixed-Workload LLM Inference
por: Sidik, Bronislav, et al.
Publicado: (2026)

Experimentally Evaluating the Resource Efficiency of Big Data Autoscaling
por: Will, Jonathan, et al.
Publicado: (2025)

TAGC: Optimizing Gradient Communication in Distributed Transformer Training
por: Polyakov, Igor, et al.
Publicado: (2025)

An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy
por: Yang, Haotian, et al.
Publicado: (2025)

Parameter-Efficient and Personalized Federated Training of Generative Models at the Edge
por: Khan, Kabir, et al.
Publicado: (2025)

Cognitive Infrastructure: A Unified DCIM Framework for AI Data Centers
por: Sunkara, Krishna Chaitanya
Publicado: (2026)

SRFed: Mitigating Poisoning Attacks in Privacy-Preserving Federated Learning with Heterogeneous Data
por: Lu, Yiwen
Publicado: (2026)

CarbonEdge: Carbon-Aware Deep Learning Inference Framework for Sustainable Edge Computing
por: Zhang, Guilin, et al.
Publicado: (2026)

A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
por: Rosendal, Daan, et al.
Publicado: (2026)

Knowledge Graphs-Driven Intelligence for Distributed Decision Systems
por: Napoli, Rosario, et al.
Publicado: (2026)

Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodes
por: Motta, Steven, et al.
Publicado: (2026)

Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning
por: Rangwala, Murtaza, et al.
Publicado: (2025)

Kant: An Efficient Unified Scheduling System for Large-Scale AI Clusters
por: Zeng, Lingling, et al.
Publicado: (2025)

Serverless GPU Architecture for Enterprise HR Analytics: A Production-Scale BDaaS Implementation
por: Zhang, Guilin, et al.
Publicado: (2025)

How Machine Learning-Data Driven Replication Strategies Enhance Fault Tolerance in Large-Scale Distributed Systems
por: Murimi, Almond Kiruthu
Publicado: (2025)

SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection
por: Ospitia, Santiago, et al.
Publicado: (2026)

A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning
por: Korkmaz, Abdulkadir, et al.
Publicado: (2025)

Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization
por: Nagoriya, Heet, et al.
Publicado: (2026)

MECKD: Deep Learning-Based Fall Detection in Multilayer Mobile Edge Computing With Knowledge Distillation
por: Mao, Wei-Lung, et al.
Publicado: (2025)

Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning
por: Petrella, Alfredo, et al.
Publicado: (2023)

ADF-LoRA: Alternating Low-Rank Aggregation for Decentralized Federated Fine-Tuning
por: Wang, Xiaoyu, et al.
Publicado: (2025)

Using Containers to Speed Up Development, to Run Integration Tests and to Teach About Distributed Systems
por: Mambelli, Marco, et al.
Publicado: (2025)

Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
por: Gaikwad, Nishant S., et al.
Publicado: (2024)

Artifact Evaluation for Distributed Systems: Current Practices and Beyond
por: Sedghpour, Mohammad Reza Saleh, et al.
Publicado: (2024)

Accelerating Geo-distributed Machine Learning with Network-Aware Adaptive Tree and Auxiliary Route
por: Li, Zonghang, et al.
Publicado: (2024)

SLO-Guard: Crash-Aware, Budget-Consistent Autotuning for SLO-Constrained LLM Serving
por: Lysenstøen, Christian
Publicado: (2026)

Cloud Uptime Archive: Open-Access Availability Data of Web, Cloud, and Gaming Services
por: Talluri, Sacheendra, et al.
Publicado: (2025)

Deadline-Aware Joint Task Scheduling and Offloading in Mobile Edge Computing Systems
por: Nguyen, Ngoc Hung, et al.
Publicado: (2025)

Feature-Aware Task-to-Core Allocation in Embedded Multi-core Platforms via Statistical Learning
por: Pivezhandi, Mohammad, et al.
Publicado: (2025)

HFedATM: Hierarchical Federated Domain Generalization via Optimal Transport and Regularized Mean Aggregation
por: Nguyen, Thinh, et al.
Publicado: (2025)

Distributed Recoverable Sketches (Extended Version)
por: Cohen, Diana, et al.
Publicado: (2025)

Cross-Platform Fused MoE Dispatch in Triton: Portable Expert Routing Without CUDA
por: Mitra, Subhadip
Publicado: (2026)

Readout-Side Bypass for Residual Hybrid Quantum-Classical Models
por: Zhang, Guilin, et al.
Publicado: (2025)

SPARK: Igniting Communication-Efficient Decentralized Learning via Stage-wise Projected NTK and Accelerated Regularization
por: Xia, Li
Publicado: (2025)

SkyNomad: On Using Multi-Region Spot Instances to Minimize AI Batch Job Cost
por: Li, Zhifei, et al.
Publicado: (2026)

FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning
por: Haque, Md Rafid, et al.
Publicado: (2025)

Shaved Ice: Optimal Compute Resource Commitments for Dynamic Multi-Cloud Workloads
por: Stokely, Murray, et al.
Publicado: (2025)

HiDVFS: A Hierarchical Multi-Agent DVFS Scheduler for OpenMP DAG Workloads
por: Pivezhandi, Mohammad, et al.
Publicado: (2026)

Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems
por: Sharma, Aasish Kumar, et al.
Publicado: (2026)