Imagen de Portada

Caesar: A Low-deviation Compression Approach for Efficient Federated Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yan, Jiaming, Liu, Jianchun, Xu, Hongli, Huang, Liusheng, Gong, Jiantao, Liu, Xudong, Hou, Kun
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Distributed, Parallel, and Cluster Computing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning
por: Li, Rukuo, et al.
Publicado: (2025)

Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
por: Wang, Shilong, et al.
Publicado: (2025)

Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
por: Huo, Yujia, et al.
Publicado: (2025)

Collaborative Speculative Inference for Efficient LLM Inference Serving
por: Gao, Luyao, et al.
Publicado: (2025)

A Robust Federated Learning Framework for Undependable Devices at Scale
por: Wang, Shilong, et al.
Publicado: (2024)

Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
por: Wang, Shilong, et al.
Publicado: (2025)

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

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

SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
por: Xu, Yang, et al.
Publicado: (2023)

Bandwidth-Aware and Cost-Efficient Pipeline Parallel Scheduling in Geo-Distributed LLM Training
por: Zhang, Han, et al.
Publicado: (2026)

ParallelSFL: A Novel Split Federated Learning Framework Tackling Heterogeneity Issues
por: Liao, Yunming, et al.
Publicado: (2024)

Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation
por: Ma, Qianpiao, et al.
Publicado: (2025)

Cross-region Model Training with Communication-Computation Overlapping and Delay Compensation
por: Zhu, Ying, et al.
Publicado: (2025)

Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning
por: Tang, Zichen, et al.
Publicado: (2024)

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
por: Zhang, Jiaojiao, et al.
Publicado: (2025)

Efficient Model Compression for Hierarchical Federated Learning
por: Zhu, Xi, et al.
Publicado: (2024)

A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning
por: Song, Jiajun, et al.
Publicado: (2024)

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices
por: Liu, Jun, et al.
Publicado: (2024)

Federated Learning in the Presence of Adversarial Client Unavailability
por: Su, Lili, et al.
Publicado: (2023)

Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
por: Zhang, Zhenxiao, et al.
Publicado: (2024)

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
por: Li, Shiwei, et al.
Publicado: (2025)

Masked Random Noise for Communication Efficient Federated Learning
por: Li, Shiwei, et al.
Publicado: (2024)

Efficient Data Distribution Estimation for Accelerated Federated Learning
por: Wang, Yuanli, et al.
Publicado: (2024)

SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning
por: Yang, Yuning, et al.
Publicado: (2024)

Byzantine-Robust and Communication-Efficient Distributed Learning via Compressed Momentum Filtering
por: Liu, Changxin, et al.
Publicado: (2024)

Adaptive Compression in Federated Learning via Side Information
por: Isik, Berivan, et al.
Publicado: (2023)

Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT
por: Wang, Heqiang, et al.
Publicado: (2024)

The Impact of Cut Layer Selection in Split Federated Learning
por: Dachille, Justin, et al.
Publicado: (2024)

Fast and Efficient 2-bit LLM Inference on GPU: 2/4/16-bit in a Weight Matrix with Asynchronous Dequantization
por: Li, Jinhao, et al.
Publicado: (2023)

Vertical Federated Learning Hybrid Local Pre-training
por: Li, Wenguo, et al.
Publicado: (2024)

Accelerating Hybrid Federated Learning Convergence under Partial Participation
por: Bian, Jieming, et al.
Publicado: (2023)

FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
por: Duan, Moming, et al.
Publicado: (2020)

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
por: Tian, Chunlin, et al.
Publicado: (2024)

Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
por: Zuo, Shiyuan, et al.
Publicado: (2024)

FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
por: Ilhan, Fatih, et al.
Publicado: (2025)

Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
por: Liu, Ruyue, et al.
Publicado: (2024)

Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
por: Crawshaw, Michael, et al.
Publicado: (2024)

FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
por: Zhou, Liuzhi, et al.
Publicado: (2024)

FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching
por: Yan, Qifan, et al.
Publicado: (2025)

Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients
por: Zhang, Zikai, et al.
Publicado: (2024)