FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Zhuohua, Liu, Maoli, Lui, John C. S. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Leveraging the Power of Conversations: Optimal Key Term Selection in Conversational Contextual Bandits
by: Liu, Maoli, et al.
Published: (2025)
by: Liu, Maoli, et al.
Published: (2025)
Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts
by: Li, Zhuohua, et al.
Published: (2025)
by: Li, Zhuohua, et al.
Published: (2025)
Federated Linear Contextual Bandits with Heterogeneous Clients
by: Blaser, Ethan, et al.
Published: (2024)
by: Blaser, Ethan, et al.
Published: (2024)
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
by: Yang, Hantao, et al.
Published: (2024)
by: Yang, Hantao, et al.
Published: (2024)
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
by: Shen, Leming, et al.
Published: (2025)
by: Shen, Leming, et al.
Published: (2025)
FedZMG: Efficient Client-Side Optimization in Federated Learning
by: Zantalis, Fotios, et al.
Published: (2026)
by: Zantalis, Fotios, et al.
Published: (2026)
FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices
by: Wang, Kaile, et al.
Published: (2026)
by: Wang, Kaile, et al.
Published: (2026)
A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses
by: Dai, Xiangxiang, et al.
Published: (2025)
by: Dai, Xiangxiang, et al.
Published: (2025)
FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection
by: Németh, Gergely D., et al.
Published: (2025)
by: Németh, Gergely D., et al.
Published: (2025)
FedPOB: Sample-Efficient Federated Prompt Optimization via Bandits
by: Lu, Pingchen, et al.
Published: (2025)
by: Lu, Pingchen, et al.
Published: (2025)
Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
by: Weng, Shudi, et al.
Published: (2025)
by: Weng, Shudi, et al.
Published: (2025)
FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
by: Wang, Xinpeng, et al.
Published: (2024)
by: Wang, Xinpeng, et al.
Published: (2024)
FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
by: Wen, Tian, et al.
Published: (2026)
by: Wen, Tian, et al.
Published: (2026)
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
by: Su, Shangchao, et al.
Published: (2023)
by: Su, Shangchao, et al.
Published: (2023)
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)
by: Islam, Md Sirajul, et al.
Published: (2024)
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
by: Ji, Xinyuan, et al.
Published: (2024)
by: Ji, Xinyuan, et al.
Published: (2024)
FedEL: Federated Elastic Learning for Heterogeneous Devices
by: Zhang, Letian, et al.
Published: (2025)
by: Zhang, Letian, et al.
Published: (2025)
Contextual Combinatorial Bandits with Probabilistically Triggered Arms
by: Liu, Xutong, et al.
Published: (2023)
by: Liu, Xutong, et al.
Published: (2023)
Fusing Reward and Dueling Feedback in Stochastic Bandits
by: Wang, Xuchuang, et al.
Published: (2025)
by: Wang, Xuchuang, et al.
Published: (2025)
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits
by: Wang, Zhiyong, et al.
Published: (2024)
by: Wang, Zhiyong, et al.
Published: (2024)
Online Clustering of Dueling Bandits
by: Wang, Zhiyong, et al.
Published: (2025)
by: Wang, Zhiyong, et al.
Published: (2025)
FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks
by: Wijethilake, Kasun Eranda, et al.
Published: (2025)
by: Wijethilake, Kasun Eranda, et al.
Published: (2025)
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)
by: Tang, Zhenheng, et al.
Published: (2024)
Large Language Model-Enhanced Multi-Armed Bandits
by: Sun, Jiahang, et al.
Published: (2025)
by: Sun, Jiahang, et al.
Published: (2025)
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
by: Peng, Zihao, et al.
Published: (2025)
by: Peng, Zihao, et al.
Published: (2025)
NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients
by: Kang, Honggu, et al.
Published: (2023)
by: Kang, Honggu, et al.
Published: (2023)
Communication-Efficient Federated Learning with Accelerated Client Gradient
by: Kim, Geeho, et al.
Published: (2022)
by: Kim, Geeho, et al.
Published: (2022)
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification
by: Chen, Hui, et al.
Published: (2024)
by: Chen, Hui, et al.
Published: (2024)
FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
by: Liu, Junkang, et al.
Published: (2026)
by: Liu, Junkang, et al.
Published: (2026)
FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective
by: Zhou, Zhekai, et al.
Published: (2025)
by: Zhou, Zhekai, et al.
Published: (2025)
Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients
by: Koo, Jabin, et al.
Published: (2024)
by: Koo, Jabin, et al.
Published: (2024)
FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity
by: Sun, Zhaobin, et al.
Published: (2024)
by: Sun, Zhaobin, et al.
Published: (2024)
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
by: Liu, Xutong, et al.
Published: (2022)
by: Liu, Xutong, et al.
Published: (2022)
Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
by: Wang, Zijian, et al.
Published: (2025)
by: Wang, Zijian, et al.
Published: (2025)
FedRD: Reducing Divergences for Generalized Federated Learning via Heterogeneity-aware Parameter Guidance
by: Wang, Kaile, et al.
Published: (2026)
by: Wang, Kaile, et al.
Published: (2026)
CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification
by: Li, Ludi, et al.
Published: (2025)
by: Li, Ludi, et al.
Published: (2025)
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
by: Seo, Jungwon, et al.
Published: (2025)
by: Seo, Jungwon, et al.
Published: (2025)
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients
by: Li, DaiXun, et al.
Published: (2023)
by: Li, DaiXun, et al.
Published: (2023)
DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models
by: Liu, Jin, et al.
Published: (2026)
by: Liu, Jin, et al.
Published: (2026)
Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
by: Ye, Wenxuan, et al.
Published: (2025)
by: Ye, Wenxuan, et al.
Published: (2025)
Similar Items
-
Leveraging the Power of Conversations: Optimal Key Term Selection in Conversational Contextual Bandits
by: Liu, Maoli, et al.
Published: (2025) -
Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts
by: Li, Zhuohua, et al.
Published: (2025) -
Federated Linear Contextual Bandits with Heterogeneous Clients
by: Blaser, Ethan, et al.
Published: (2024) -
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
by: Yang, Hantao, et al.
Published: (2024) -
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
by: Shen, Leming, et al.
Published: (2025)