Copertina

Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wu, Zhiyuan, Sun, Sheng, Wang, Yuwei, Liu, Min, Xu, Ke, Pan, Quyang, Gao, Bo, Wen, Tian
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Distributed, Parallel, and Cluster Computing Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
di: Wu, Zhiyuan, et al.
Pubblicazione: (2023)

FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence
di: Wu, Zhiyuan, et al.
Pubblicazione: (2023)

SVAFD: A Secure and Verifiable Co-Aggregation Protocol for Federated Distillation
di: Wen, Tian, et al.
Pubblicazione: (2025)

Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge
di: Wu, Yebo, et al.
Pubblicazione: (2026)

EC2MoE: Adaptive End-Cloud Pipeline Collaboration Enabling Scalable Mixture-of-Experts Inference
di: Yang, Zheming, et al.
Pubblicazione: (2025)

Recursive Offloading for LLM Serving in Multi-tier Networks
di: Wu, Zhiyuan, et al.
Pubblicazione: (2025)

Distributed Resource Selection for Self-Organising Cloud-Edge Systems
di: Renau, Quentin, et al.
Pubblicazione: (2025)

Cost-Effective Edge Data Distribution with End-To-End Delay Guarantees in Edge Computing
di: Shankar, Ravi, et al.
Pubblicazione: (2025)

Scaling Real-Time Traffic Analytics on Edge-Cloud Fabrics for City-Scale Camera Networks
di: Sharma, Akash, et al.
Pubblicazione: (2026)

Edge-Cloud Collaborative Pothole Detection via Onboard Event Screening and Federated Temporal Segmentation
di: Wu, Yingjie, et al.
Pubblicazione: (2026)

H-EYE: Holistic Resource Modeling and Management for Diversely Scaled Edge-Cloud Systems
di: Dagli, Ismet, et al.
Pubblicazione: (2024)

Distributed Edge Analytics in Edge-Fog-Cloud Continuum
di: Srirama, Satish Narayana
Pubblicazione: (2024)

Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
di: Wu, Zhiyuan, et al.
Pubblicazione: (2024)

Learnable Sparse Customization in Heterogeneous Edge Computing
di: Xue, Jingjing, et al.
Pubblicazione: (2024)

M$^2$-MFP: A Multi-Scale and Multi-Level Memory Failure Prediction Framework for Reliable Cloud Infrastructure
di: Xie, Hongyi, et al.
Pubblicazione: (2025)

Accelerating End-Cloud Collaborative Inference via Near Bubble-free Pipeline Optimization
di: Gao, Luyao, et al.
Pubblicazione: (2024)

Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Models
di: Gao, Wentao, et al.
Pubblicazione: (2024)

A Decentralized Microservice Scheduling Approach Using Service Mesh in Cloud-Edge Systems
di: Wen, Yangyang, et al.
Pubblicazione: (2025)

Adaptive Configuration Selection for Multi-Model Inference Pipelines in Edge Computing
di: Sheng, Jinhao, et al.
Pubblicazione: (2025)

A Pipelined Collaborative Speculative Decoding Framework for Efficient Edge-Cloud LLM Inference
di: Zhang, Yida, et al.
Pubblicazione: (2026)

GeoFaaS: An Edge-to-Cloud FaaS Platform
di: Malekabbasi, Mohammadreza, et al.
Pubblicazione: (2024)

KCES: A Workflow Containerization Scheduling Scheme Under Cloud-Edge Collaboration Framework
di: Shan, Chenggang, et al.
Pubblicazione: (2024)

Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scaling
di: Fang, Bruce, et al.
Pubblicazione: (2025)

Scaling LLM Inference Beyond Amdahl`s Limits via Eliminating Non-Scalable Overheads
di: Zhao, Alan, et al.
Pubblicazione: (2026)

ACE-Sync: An Adaptive Cloud-Edge Synchronization Framework for Communication-Efficient Large-Scale Distributed Model Training
di: Yang, Yi, et al.
Pubblicazione: (2025)

AIGC-assisted Federated Learning for Vehicular Edge Intelligence: Vehicle Selection, Resource Allocation and Model Augmentation
di: Qiang, Xianke, et al.
Pubblicazione: (2025)

PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems
di: Sun, Wei, et al.
Pubblicazione: (2026)

PICE: A Semantic-Driven Progressive Inference System for LLM Serving in Cloud-Edge Networks
di: Zhan, Huiyou, et al.
Pubblicazione: (2025)

PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding
di: Han, Yunhe, et al.
Pubblicazione: (2026)

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence
di: Qiang, Xianke, et al.
Pubblicazione: (2026)

EMO: Edge Model Overlays to Scale Model Size in Federated Learning
di: Wu, Di, et al.
Pubblicazione: (2025)

Split Federated Learning Empowered Vehicular Edge Intelligence: Concept, Adaptive Design and Future Directions
di: Qiang, Xianke, et al.
Pubblicazione: (2024)

Application-level observability for adaptive Edge to Cloud continuum systems
di: Sidi, Kaddour, et al.
Pubblicazione: (2026)

Towards Seamless Serverless Computing Across an Edge-Cloud Continuum
di: Simion, Emilian, et al.
Pubblicazione: (2024)

GMB-ECC: Guided Measuring and Benchmarking of the Edge Cloud Continuum
di: Jahnke, Brian-Frederik, et al.
Pubblicazione: (2025)

ContinuumConductor : Decentralized Process Mining on the Edge-Cloud Continuum
di: Reiter, Hendrik, et al.
Pubblicazione: (2025)

Object Abstraction To Streamline Edge-Cloud-Native Application Development
di: Lertpongrujikorn, Pawissanutt
Pubblicazione: (2025)

Administrative Decentralization in Edge-Cloud Multi-Agent for Mobile Automation
di: Li, Senyao, et al.
Pubblicazione: (2026)

Bridging Memory Gaps: Scaling Federated Learning for Heterogeneous Clients
di: Wu, Yebo, et al.
Pubblicazione: (2024)

MoA-Off: Adaptive Heterogeneous Modality-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
di: Yang, Zheming, et al.
Pubblicazione: (2025)