:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Cheng, Jiacheng, Zhang, Xu, Qiu, Guanghui, Zhang, Yifang, Li, Yinchuan, Feng, Kaiyuan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2511.13144
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
by: Zhang, Xu, et al.
Published: (2023)

UltraSketchLLM: Saliency-Driven Sketching for Ultra-Low Bit LLM Compression
by: Zou, Sunan, et al.
Published: (2025)

Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching
by: Gattani, Vineet Sunil, et al.
Published: (2024)

MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
by: Li, Xin-Chun, et al.
Published: (2024)

Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
by: Zhang, Meifan, et al.
Published: (2024)

Federated Contrastive Learning for Personalized Semantic Communication
by: Wang, Yining, et al.
Published: (2024)

One-Bit Quantization for Random Features Models
by: Akhtiamov, Danil, et al.
Published: (2025)

FedBiF: Communication-Efficient Federated Learning via Bits Freezing
by: Li, Shiwei, et al.
Published: (2025)

Random Cycle Coding: Lossless Compression of Cluster Assignments via Bits-Back Coding
by: Severo, Daniel, et al.
Published: (2024)

Robust and Communication-Efficient Federated Domain Adaptation via Random Features
by: Feng, Zhanbo, et al.
Published: (2023)

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

One-shot Federated Learning via Synthetic Distiller-Distillate Communication
by: Zhang, Junyuan, et al.
Published: (2024)

Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs
by: Xu, Binxing, et al.
Published: (2026)

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization
by: Yi, Kai
Published: (2025)

Personalized One-shot Federated Graph Learning for Heterogeneous Clients
by: Yan, Guochen, et al.
Published: (2024)

Learning Grouped Lattice Vector Quantizers for Low-Bit LLM Compression
by: Zhang, Xi, et al.
Published: (2025)

Sketched Gaussian Mechanism for Private Federated Learning
by: Li, Qiaobo, et al.
Published: (2025)

SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening
by: Rangwala, Murtaza, et al.
Published: (2025)

Scaling Federated Linear Contextual Bandits via Sketching
by: Yang, Hantao, et al.
Published: (2026)

Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
by: Wei, Ting, et al.
Published: (2025)

Asynchronous Personalized Federated Learning through Global Memorization
by: Wan, Fan, et al.
Published: (2025)

Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis
by: Chen, Zhijie, et al.
Published: (2024)

Few-for-Many Personalized Federated Learning
by: Guo, Ping, et al.
Published: (2026)

Spectral Sentinel: Scalable Byzantine-Robust Decentralized Federated Learning via Sketched Random Matrix Theory on Blockchain
by: Mishra, Animesh
Published: (2025)

Personalized Federated Instruction Tuning via Neural Architecture Search
by: Zhang, Pengyu, et al.
Published: (2024)

Hijack Vertical Federated Learning Models As One Party
by: Qiu, Pengyu, et al.
Published: (2022)

Sheaf HyperNetworks for Personalized Federated Learning
by: Nguyen, Bao, et al.
Published: (2024)

Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
by: Lv, Fengling, et al.
Published: (2024)

Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing
by: Chen, Cheng, et al.
Published: (2021)

Personalized Federated Continual Learning via Multi-granularity Prompt
by: Yu, Hao, et al.
Published: (2024)

Client-supervised Federated Learning: Towards One-model-for-all Personalization
by: Yan, Peng, et al.
Published: (2024)

Stochastic Controlled Averaging for Federated Learning with Communication Compression
by: Huang, Xinmeng, et al.
Published: (2023)

Revisiting Matrix Sketching in Linear Bandits: Achieving Sublinear Regret via Dyadic Block Sketching
by: Wen, Dongxie, et al.
Published: (2024)

Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective
by: Qin, Zhen, et al.
Published: (2023)

Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
by: Zhang, Tianyi, et al.
Published: (2024)

Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth
by: Zhuansun, Ying, et al.
Published: (2024)

Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learning
by: Chen, Weihang, et al.
Published: (2024)

WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models
by: Feng, Tao, et al.
Published: (2025)

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

CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification
by: Li, Ludi, et al.
Published: (2025)