Copertina

Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Cheng, Jiacheng, Zhang, Xu, Qiu, Guanghui, Zhang, Yifang, Li, Yinchuan, Feng, Kaiyuan
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

MaxSketch: Robust Distinct Counting in Streams via Random Projections
di: Tsikouras, Nikos, et al.
Pubblicazione: (2026)

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

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

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

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

Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences
di: Liu, Shudong, et al.
Pubblicazione: (2026)