Image de couverture de livre

SuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-Networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Asif, Abdullah Al, Yu, Sixing, Munoz, Juan Pablo, Mazaheri, Arya, Jannesari, Ali
Format:	Preprint
Publié:	2026
Sujets:	Distributed, Parallel, and Cluster Computing
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

HeLoCo: Efficient asynchronous low-communication training under data and device heterogeneity
par: Asif, Abdullah Al, et autres
Publié: (2026)

PipeInfer: Accelerating LLM Inference using Asynchronous Pipelined Speculation
par: Butler, Branden, et autres
Publié: (2024)

ParallelSFL: A Novel Split Federated Learning Framework Tackling Heterogeneity Issues
par: Liao, Yunming, et autres
Publié: (2024)

AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
par: Lin, Zheng, et autres
Publié: (2024)

Personalized Federated Learning on Data with Dynamic Heterogeneity under Limited Storage
par: Tan, Sixing, et autres
Publié: (2024)

SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
par: Xu, Yang, et autres
Publié: (2023)

Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Prototypes
par: Siddika, Fatema, et autres
Publié: (2025)

Dynamic Detection of Inefficient Data Mapping Patterns in Heterogeneous OpenMP Applications
par: Marzen, Luke, et autres
Publié: (2026)

SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework
par: Wu, Jiasheng, et autres
Publié: (2025)

MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation
par: Liao, Yunming, et autres
Publié: (2023)

Static Generation of Efficient OpenMP Offload Data Mappings
par: Marzen, Luke, et autres
Publié: (2024)

Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing
par: Zhang, Mingjin, et autres
Publié: (2024)

SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data
par: Kapadia, Shashank, et autres
Publié: (2026)

Decentralized Proactive Model Offloading and Resource Allocation for Split and Federated Learning
par: Huang, Binbin, et autres
Publié: (2024)

Knowledge-Aware Evolution for Streaming Federated Continual Learning with Category Overlap and without Task Identifiers
par: Tan, Sixing, et autres
Publié: (2026)

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices
par: Chen, Xiaopei, et autres
Publié: (2025)

Communication-and-Computation Efficient Split Federated Learning: Gradient Aggregation and Resource Management
par: Liang, Yipeng, et autres
Publié: (2025)

Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack
par: Yang, Qiantao, et autres
Publié: (2026)

Convergence Analysis of Split Federated Learning on Heterogeneous Data
par: Han, Pengchao, et autres
Publié: (2024)

MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations
par: Dutta, Akash, et autres
Publié: (2024)

Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence
par: Qiang, Xianke, et autres
Publié: (2026)

Optimizing Resource Allocation and Energy Efficiency in Federated Fog Computing for IoT
par: Shah, Syed Sarmad, et autres
Publié: (2025)

Data Heterogeneity-Aware Client Selection for Federated Learning in Wireless Networks
par: Yang, Yanbing, et autres
Publié: (2025)

Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Models
par: Gao, Wentao, et autres
Publié: (2024)

The Case for Time-Shared Computing Resources
par: Jacquet, Pierre, et autres
Publié: (2025)

Non-Federated Multi-Task Split Learning for Heterogeneous Sources
par: Zheng, Yilin, et autres
Publié: (2024)

A Scalable State Sharing Protocol for Low-Resource Validator Nodes in Blockchain Networks
par: Hias, Ruben, et autres
Publié: (2024)

HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction
par: Sarkar, Aishwarya, et autres
Publié: (2025)

Huawei Cloud Model-as-a-Service on the CloudMatrix384 SuperPod
par: Xiao, Ao, et autres
Publié: (2025)

SuperBench: Improving Cloud AI Infrastructure Reliability with Proactive Validation
par: Xiong, Yifan, et autres
Publié: (2024)

Adventures with Grace Hopper AI Super Chip and the National Research Platform
par: Hurt, J. Alex, et autres
Publié: (2024)

Robust Federated Fine-Tuning in Heterogeneous Networks with Unreliable Connections: An Aggregation View
par: Wang, Yanmeng, et autres
Publié: (2025)

Split Learning without Local Weight Sharing to Enhance Client-side Data Privacy
par: Pham, Ngoc Duy, et autres
Publié: (2022)

DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
par: Zhang, Hongliang, et autres
Publié: (2025)

FedFog: Resource-Aware Federated Learning in Edge and Fog Networks
par: Sobati-M, Somayeh
Publié: (2025)

Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
par: Tian, Chunlin, et autres
Publié: (2024)

Trace Replay Simulation of MIT SuperCloud for Studying Optimal Sustainability Policies
par: Brewer, Wesley, et autres
Publié: (2025)

Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network
par: Zhao, Haitao, et autres
Publié: (2026)

Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks
par: Yu, Lu, et autres
Publié: (2024)

Federated Inference for Heterogeneous LLM Communication and Collaboration
par: Chen, Zihan, et autres
Publié: (2026)