:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lu, Rongwei, Jiang, Yutong, Zhang, Jinrui, Li, Chunyang, Zhu, Yifei, Chen, Bin, Wang, Zhi
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2505.12479
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing
by: Lu, Rongwei, et al.
Published: (2023)

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

Taming Latency and Bandwidth: A Theoretical Framework and Adaptive Algorithm for Communication-Constrained Training
by: Lu, Rongwei, et al.
Published: (2025)

FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
by: He, Yutong, et al.
Published: (2026)

Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
by: de Vazelhes, William, et al.
Published: (2022)

Posterior Approximation using Stochastic Gradient Ascent with Adaptive Stepsize
by: Lim, Kart-Leong, et al.
Published: (2024)

FedSGM: A Unified Framework for Constraint Aware, Bidirectionally Compressed, Multi-Step Federated Optimization
by: Upadhyay, Antesh, et al.
Published: (2026)

Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
by: Rajpal, Rajit, et al.
Published: (2025)

Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning
by: Gao, Chengqian, 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)

FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation
by: Li, Xingyu, et al.
Published: (2021)

FedOAED: Federated On-Device Autoencoder Denoiser for Heterogeneous Data under Limited Client Availability
by: Howlader, S M Ruhul Kabir, et al.
Published: (2025)

FedAgg: Adaptive Federated Learning with Aggregated Gradients
by: Yuan, Wenhao, et al.
Published: (2023)

Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
by: Wu, Jingfeng, et al.
Published: (2024)

CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
by: Wu, Huiwen, et al.
Published: (2024)

SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation
by: Xu, Haotian, et al.
Published: (2025)

FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
by: Lu, Chaoyi, et al.
Published: (2026)

FedGIG: Graph Inversion from Gradient in Federated Learning
by: Xiao, Tianzhe, et al.
Published: (2024)

Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
by: Chen, Chuyan, et al.
Published: (2025)

Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
by: Su, Xiaoxin, et al.
Published: (2024)

Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
by: Wu, Jingfeng, et al.
Published: (2025)

Negative Stepsizes Make Gradient-Descent-Ascent Converge
by: Shugart, Henry, et al.
Published: (2025)

FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning
by: Lu, Xiuhua, et al.
Published: (2024)

FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning
by: Devkota, Alina, et al.
Published: (2026)

FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet
by: Pautrel, Thibault, et al.
Published: (2026)

FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation
by: Xiao, Chenguang, et al.
Published: (2024)

Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations
by: Meng, Bin, et al.
Published: (2024)

FedDSR: Federated Deep Supervision and Regularization Towards Autonomous Driving
by: Kou, Wei-Bin, et al.
Published: (2025)

SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning
by: Shiranthika, Chamani, et al.
Published: (2024)

FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
by: Chen, Tan, et al.
Published: (2025)

FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning
by: Zhou, Zhanting, et al.
Published: (2025)

New Insight of Variance reduce in Zero-Order Hard-Thresholding: Mitigating Gradient Error and Expansivity Contradictions
by: Yuan, Xinzhe, et al.
Published: (2026)

Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes
by: Zhang, Ruiqi, et al.
Published: (2025)

FedMerge: Federated Personalization via Model Merging
by: Chen, Shutong, et al.
Published: (2025)

FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning
by: Xiao, Bingnan, et al.
Published: (2026)

FedRIR: Rethinking Information Representation in Federated Learning
by: Huang, Yongqiang, et al.
Published: (2025)

Can Textual Gradient Work in Federated Learning?
by: Chen, Minghui, et al.
Published: (2025)

Normalized Iterative Hard Thresholding for Tensor Recovery
by: Li, Li, et al.
Published: (2025)

GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
by: Seo, Jungwon, et al.
Published: (2025)

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