:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Yan, Zhang, Xiao, Li, Mingyi, Xu, Guangwei, Chen, Feng, Yuan, Yuan, Zou, Yifei, Zhao, Mengying, Lu, Jianbo, Yu, Dongxiao
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	https://arxiv.org/abs/2410.08457
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Distributed Bilevel Optimization with Dual Pruning for Resource-limited Clients
by: Li, Mingyi, et al.
Published: (2025)

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

A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments
by: Zhang, Ruirui, et al.
Published: (2024)

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning
by: Li, Nan, et al.
Published: (2025)

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
by: Wang, Xiaolu, et al.
Published: (2024)

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

FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation
by: Howlader, S M Ruhul Kabir, et al.
Published: (2026)

Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks
by: Zhai, Wenbin, et al.
Published: (2023)

SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
by: Islam, Md Sirajul, et al.
Published: (2025)

PruneX: A Hierarchical Communication-Efficient System for Distributed CNN Training with Structured Pruning
by: Olama, Alireza, et al.
Published: (2025)

An Experimental Study of Different Aggregation Schemes in Semi-Asynchronous Federated Learning
by: Li, Yunbo, et al.
Published: (2024)

EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control
by: Li, Xiaochen, et al.
Published: (2024)

VDCores: Resource Decoupled Programming and Execution for Asynchronous GPU
by: He, Zijian, et al.
Published: (2026)

CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
by: Zhang, Yu, et al.
Published: (2021)

COoL-TEE: Client-TEE Collaboration for Resilient Distributed Search
by: Bettinger, Matthieu, et al.
Published: (2025)

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning
by: Yu, Liangkun, et al.
Published: (2024)

Hyperion: Low-Latency Ultra-HD Video Analytics via Collaborative Vision Transformer Inference
by: Jiang, Linyi, et al.
Published: (2025)

Unleashing Efficient Asynchronous RL Post-Training via Staleness-Constrained Rollout Coordination
by: Li, Haoyang, et al.
Published: (2026)

GPU Memory and Utilization Estimation for Training-Aware Resource Management: Opportunities and Limitations
by: Yousefzadeh-Asl-Miandoab, Ehsan, et al.
Published: (2026)

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

Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices
by: Chen, Fahao, et al.
Published: (2025)

FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
by: Li, Yunbo, et al.
Published: (2025)

HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware
by: Yan, Ran, et al.
Published: (2024)

Pollen: High-throughput Federated Learning Simulation via Resource-Aware Client Placement
by: Sani, Lorenzo, et al.
Published: (2023)

GAS: Generative Activation-Aided Asynchronous Split Federated Learning
by: Yang, Jiarong, et al.
Published: (2024)

Falcon: Advancing Asynchronous BFT Consensus for Lower Latency and Enhanced Throughput
by: Dai, Xiaohai, et al.
Published: (2025)

CaPGNN: Optimizing Parallel Graph Neural Network Training with Joint Caching and Resource-Aware Graph Partitioning
by: Song, Xianfeng, et al.
Published: (2025)

AMDP: Asynchronous Multi-Directional Pipeline Parallelism for Large-Scale Models Training
by: Chen, Ling, et al.
Published: (2026)

Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analytics
by: Yuan, Yichao, et al.
Published: (2025)

AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs
by: Yan, Ran, et al.
Published: (2025)

Many Hands Make Light Work: Accelerating Edge Inference via Multi-Client Collaborative Caching
by: Liang, Wenyi, et al.
Published: (2024)

Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems
by: Li, Yilin, et al.
Published: (2025)

Lemonshark: Asynchronous DAG-BFT With Early Finality
by: Hu, Michael Yiqing, et al.
Published: (2026)

Amortized Asynchronous Byzantine Reliable Broadcast with Optimal Resilience
by: Hu, Michael Yiqing, et al.
Published: (2026)

Efficient CPU-GPU Collaborative Inference for MoE-based LLMs on Memory-Limited Systems
by: Huang, En-Ming, et al.
Published: (2025)

Radiation Hydrodynamics at Scale: Comparing MPI and Asynchronous Many-Task Runtimes with FleCSI
by: Strack, Alexander, et al.
Published: (2026)

Lagom: Unleashing the Power of Communication and Computation Overlapping for Distributed LLM Training
by: Xu, Guanbin, et al.
Published: (2026)

FedAPTA: Federated Multi-task Learning for Heterogeneous Devices with Adaptive Layer-wise Pruning and Task-aware Aggregation
by: Yu, Zhen, et al.
Published: (2025)

Semi-decentralized Federated Time Series Prediction with Client Availability Budgets
by: Bao, Yunkai, et al.
Published: (2025)

SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices
by: Cao, Linxiao, et al.
Published: (2024)