Cover Image

FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Haonan, Liu, Zeli, Hoshino, Kajimusugura, Zhang, Tuo, Walters, John Paul, Crago, Stephen
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Beyond One-Way Pruning: Bidirectional Pruning-Regrowth for Extreme Accuracy-Sparsity Tradeoff
by: Liu, Junchen, et al.
Published: (2025)

TRIM: Achieving Extreme Sparsity with Targeted Row-wise Iterative Metric-driven Pruning
by: Beck, Florentin, et al.
Published: (2025)

FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
by: Huang, Hong, et al.
Published: (2025)

FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning
by: Herzog, Alexander, et al.
Published: (2024)

Active Testing of Large Language Models via Approximate Neyman Allocation
by: Liu, Zeli, et al.
Published: (2026)

FedMuon: Accelerating Federated Learning with Matrix Orthogonalization
by: Liu, Junkang, et al.
Published: (2025)

FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification
by: Yu, Po-Hsien, et al.
Published: (2025)

Sparsity Induction for Accurate Post-Training Pruning of Large Language Models
by: Jiang, Minhao, et al.
Published: (2026)

FedNSAM:Consistency of Local and Global Flatness for Federated Learning
by: Liu, Junkang, et al.
Published: (2026)

Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity
by: Guo, Wentao, et al.
Published: (2024)

One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models
by: Shao, Hang, et al.
Published: (2023)

Extreme Model Compression with Structured Sparsity at Low Precision
by: Liu, Dan, et al.
Published: (2025)

Navigating Extremes: Dynamic Sparsity in Large Output Spaces
by: Ullah, Nasib, et al.
Published: (2024)

Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization
by: Eccles, Bailey J., et al.
Published: (2024)

FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
by: Li, Zhuohua, et al.
Published: (2024)

FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
by: Ji, Xinyuan, et al.
Published: (2024)

AdaFed: Fair Federated Learning via Adaptive Common Descent Direction
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Weight Concentration Regularization for Improving Pruning Robustness Under High Sparsity
by: Yun, Vincent-Daniel, et al.
Published: (2025)

FedTrans: Efficient Federated Learning via Multi-Model Transformation
by: Zhu, Yuxuan, et al.
Published: (2024)

FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
by: Han, Shanshan, et al.
Published: (2023)

Multilingual Encoder Knows more than You Realize: Shared Weights Pretraining for Extremely Low-Resource Languages
by: Su, Zeli, et al.
Published: (2025)

Fed-Credit: Robust Federated Learning with Credibility Management
by: Chen, Jiayan, et al.
Published: (2024)

FedBWO: Enhancing Communication Efficiency in Federated Learning
by: Hayyolalam, Vahideh, et al.
Published: (2025)

FedEL: Federated Elastic Learning for Heterogeneous Devices
by: Zhang, Letian, et al.
Published: (2025)

FedModule: A Modular Federated Learning Framework
by: Chen, Chuyi, et al.
Published: (2024)

FedEGG: Federated Learning with Explicit Global Guidance
by: Zhai, Kun, et al.
Published: (2024)

FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
by: Li, Youpeng, et al.
Published: (2024)

DP-FedPGN: Finding Global Flat Minima for Differentially Private Federated Learning via Penalizing Gradient Norm
by: Liu, Junkang, et al.
Published: (2025)

FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
by: Liu, Xiang, et al.
Published: (2023)

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
by: Peng, Zihao, et al.
Published: (2025)

FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
by: Shen, Leming, et al.
Published: (2025)

Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs
by: Gao, Chang, et al.
Published: (2025)

FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning
by: Tamirisa, Rishub, et al.
Published: (2023)

FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
by: Liu, Junkang, et al.
Published: (2026)

Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training
by: Wang, Zeli, et al.
Published: (2026)

FedSDR: Federated Self-Distillation with Rectification
by: Ren, Ziheng, et al.
Published: (2026)

FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning
by: Zhou, Yanbing, et al.
Published: (2025)

FedPS: Federated data Preprocessing via aggregated Statistics
by: Xu, Xuefeng, et al.
Published: (2026)

FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing
by: Zhao, Haodong, et al.
Published: (2025)

Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
by: Pang, Jing-Cheng, et al.
Published: (2021)