Cover Image

Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Tianyi, Cao, Yu, Liu, Dianbo
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records
by: Zhang, Tianyi, et al.
Published: (2021)

Contrastive Federated Learning with Tabular Data Silos
by: Ginanjar, Achmad, et al.
Published: (2024)

Graph Learning Across Data Silos
by: Zhang, Xiang, et al.
Published: (2023)

ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
by: Kato, Fumiyuki, et al.
Published: (2023)

Early Quantization Shrinks Codebook: A Simple Fix for Diversity-Preserving Tokenization
by: Zhao, Wenhao, et al.
Published: (2026)

Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
by: Kuo, Kevin, et al.
Published: (2025)

Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
by: Chen, Minghui, et al.
Published: (2024)

Mitigating Premature Discretization with Progressive Quantization for Robust Vector Tokenization
by: Zhao, Wenhao, et al.
Published: (2026)

Discrete Codebook World Models for Continuous Control
by: Scannell, Aidan, et al.
Published: (2025)

Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models
by: Wu, Yudi, et al.
Published: (2025)

Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning
by: Chen, Mengmeng, et al.
Published: (2024)

Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning
by: Das, Anirban, et al.
Published: (2021)

Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
by: Yang, Yuwen, et al.
Published: (2024)

FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices
by: Wang, Kaile, et al.
Published: (2026)

How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
by: Sun, Yuchang, et al.
Published: (2024)

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning
by: Elhussein, Ahmed, et al.
Published: (2025)

Client Selection in Federated Learning with Data Heterogeneity and Network Latencies
by: Vardhan, Harsh, et al.
Published: (2025)

Adaptive Guidance for Local Training in Heterogeneous Federated Learning
by: Zhang, Jianqing, et al.
Published: (2024)

Dynamic Clustering for Personalized Federated Learning on Heterogeneous Edge Devices
by: Liu, Heting, et al.
Published: (2025)

Improving Discrete Optimisation Via Decoupled Straight-Through Estimator
by: Shah, Rushi, et al.
Published: (2024)

SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation
by: Vetcha, Nitin, et al.
Published: (2026)

FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
by: Li, Zilinghan, et al.
Published: (2023)

Enhance Eye Disease Detection using Learnable Probabilistic Discrete Latents in Machine Learning Architectures
by: Prabhakaran, Anirudh, et al.
Published: (2024)

Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
by: Li, Zhilong, et al.
Published: (2024)

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices
by: Li, Hangyu, et al.
Published: (2025)

Federated Reinforcement Learning with Constraint Heterogeneity
by: Jin, Hao, et al.
Published: (2024)

Rethinking the initialization of Momentum in Federated Learning with Heterogeneous Data
by: Xiao, Chenguang, et al.
Published: (2024)

Sharp Bounds for Sequential Federated Learning on Heterogeneous Data
by: Li, Yipeng, et al.
Published: (2024)

Federated Online Learning for Heterogeneous Multisource Streaming Data
by: Li, Jingmao, et al.
Published: (2025)

Data Heterogeneity and Forgotten Labels in Split Federated Learning
by: Tirana, Joana, et al.
Published: (2025)

Communication-Efficient Federated Learning With Data and Client Heterogeneity
by: Zakerinia, Hossein, et al.
Published: (2022)

Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
by: Li, Yipeng, et al.
Published: (2023)

Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data
by: Liu, Jie, et al.
Published: (2025)

Awesome-OL: An Extensible Toolkit for Online Learning
by: Liu, Zeyi, et al.
Published: (2025)

FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
by: Yang, Zhiqin, et al.
Published: (2025)

Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources
by: Li, Jinlong, et al.
Published: (2024)

Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings
by: Arazzi, Marco, et al.
Published: (2025)

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

Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data
by: Li, Yunfeng, et al.
Published: (2025)

Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
by: Li, Zilinghan, et al.
Published: (2024)