Cover Image

Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Koo, Jabin, Kim, Hoyoung, Jang, Minwoo, Ok, Jungseul
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients
by: Koo, Jabin, et al.
Published: (2024)

SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment
by: Zhu, Wenqiao, et al.
Published: (2025)

Variational Bayes for Federated Continual Learning
by: Yao, Dezhong, et al.
Published: (2024)

Influence-oriented Personalized Federated Learning
by: Tan, Yue, et al.
Published: (2024)

Federated Graph Learning with Structure Proxy Alignment
by: Fu, Xingbo, et al.
Published: (2024)

POPri: Private Federated Learning using Preference-Optimized Synthetic Data
by: Hou, Charlie, et al.
Published: (2025)

From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
by: Wu, Xinghao, et al.
Published: (2026)

FedAH: Aggregated Head for Personalized Federated Learning
by: Zhou, Pengzhan, et al.
Published: (2024)

Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
by: Lee, Seunghyun, et al.
Published: (2025)

Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity
by: Wang, Zheng, et al.
Published: (2024)

A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
by: Chen, Minghui, et al.
Published: (2025)

DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models
by: Chang, Ying, et al.
Published: (2025)

Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning
by: Chen, Sijia, et al.
Published: (2024)

Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
by: Liu, Ruyue, et al.
Published: (2024)

FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data
by: AbouNassar, Eman M., et al.
Published: (2026)

Convergence Analysis of Federated Learning Methods Using Backward Error Analysis
by: Lim, Jinwoo, et al.
Published: (2025)

Co-LoRA: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients
by: Seo, Minhyuk, et al.
Published: (2025)

FedUV: Uniformity and Variance for Heterogeneous Federated Learning
by: Son, Ha Min, et al.
Published: (2024)

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

SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead
by: Kim, Minsu, et al.
Published: (2024)

FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences
by: Hartmann, Maria, et al.
Published: (2025)

Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data
by: Son, Ha Min, et al.
Published: (2021)

MQ-GNN: A Multi-Queue Pipelined Architecture for Scalable and Efficient GNN Training
by: Ullah, Irfan, et al.
Published: (2026)

EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning
by: Wang, Shuo, et al.
Published: (2023)

Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks
by: Deng, Xiumei, et al.
Published: (2025)

Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation
by: Shin, Yujin, et al.
Published: (2024)

Trust-free Personalized Decentralized Learning
by: Li, Yawen, et al.
Published: (2024)

Federated Multi-Objective Learning
by: Yang, Haibo, et al.
Published: (2023)

Federated Learning with Flexible Architectures
by: Park, Jong-Ik, et al.
Published: (2024)

On Using Large-Batches in Federated Learning
by: Tyagi, Sahil
Published: (2025)

Tackling Selfish Clients in Federated Learning
by: Augello, Andrea, et al.
Published: (2024)

DFML: Decentralized Federated Mutual Learning
by: Khalil, Yasser H., et al.
Published: (2024)

Federated Learning with Limited Node Labels
by: Tang, Bisheng, et al.
Published: (2024)

Uncertainty-Aware Explainable Federated Learning
by: Zhang, Yanci, et al.
Published: (2025)

Efficient Client Selection in Federated Learning
by: Marfo, William, et al.
Published: (2025)

Clust-PSI-PFL: A Population Stability Index Approach for Clustered Non-IID Personalized Federated Learning
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2025)

Efficient Federated Learning with Timely Update Dissemination
by: Jia, Juncheng, et al.
Published: (2025)

Efficient Asynchronous Federated Learning with Sparsification and Quantization
by: Jia, Juncheng, et al.
Published: (2023)

On the Byzantine-Resilience of Distillation-Based Federated Learning
by: Roux, Christophe, et al.
Published: (2024)

Federated style aware transformer aggregation of representations
by: Jeon, Mincheol, et al.
Published: (2025)