Cover Image

TriCon-SF: A Triple-Shuffle and Contribution-Aware Serial Federated Learning Framework for Heterogeneous Healthcare Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yan, Yuping, Wang, Yizhi, Li, Yuanshuai, Jin, Yaochu
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

IRIS: Implicit Reward-Guided Internal Sifting for Mitigating Multimodal Hallucination
by: Li, Yuanshuai, et al.
Published: (2026)

HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization
by: Yan, Yuping, et al.
Published: (2026)

Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients
by: Wu, Leming, et al.
Published: (2025)

Federated Loss Exploration for Improved Convergence on Non-IID Data
by: Internò, Christian, et al.
Published: (2025)

On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks
by: Milasheuski, Usevalad, et al.
Published: (2024)

LacaDM: A Latent Causal Diffusion Model for Multiobjective Reinforcement Learning
by: Yan, Xueming, et al.
Published: (2025)

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
by: Xu, Yukai, et al.
Published: (2024)

Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks
by: Yu, Yingchao, et al.
Published: (2024)

Personalized Federated Dictionary Learning for Modeling Heterogeneity in Multi-site fMRI Data
by: Zhang, Yipu, et al.
Published: (2025)

FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
by: Li, Zhuohua, et al.
Published: (2024)

History-Aware and Dynamic Client Contribution in Federated Learning
by: Ghosh, Bishwamittra, et al.
Published: (2024)

Federated Learning on Virtual Heterogeneous Data with Local-global Distillation
by: Huang, Chun-Yin, et al.
Published: (2023)

Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks
by: Zhan, Shichen, et al.
Published: (2024)

LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2025)

Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach
by: Xu, Changxin, et al.
Published: (2024)

Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
by: Weng, Shudi, et al.
Published: (2025)

Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environment
by: Liu, Yuze, et al.
Published: (2025)

Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
by: Chen, Mengmeng, et al.
Published: (2025)

Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2024)

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2024)

Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis
by: Kim, June-Woo, et al.
Published: (2025)

Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms
by: Yang, Haowei, et al.
Published: (2024)

TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency
by: Imteaj, Ahmed, et al.
Published: (2024)

Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
by: Zuo, Shiyuan, et al.
Published: (2024)

Progressive Size-Adaptive Federated Learning: A Comprehensive Framework for Heterogeneous Multi-Modal Data Systems
by: Hussain, Sajid, et al.
Published: (2025)

Probabilistic Federated Learning on Uncertain and Heterogeneous Data with Model Personalization
by: Rahman, Ratun, et al.
Published: (2026)

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

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data
by: Rokvic, Ljubomir, et al.
Published: (2025)

Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
by: Zhou, Tailin, et al.
Published: (2023)

Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments
by: Sun, Pengcheng, et al.
Published: (2025)

Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle
by: Zhu, Linghao, et al.
Published: (2025)

Reinforcement Learning for Scalable Train Timetable Rescheduling with Graph Representation
by: Yue, Peng, et al.
Published: (2024)

Accurate Forgetting for Heterogeneous Federated Continual Learning
by: Wuerkaixi, Abudukelimu, et al.
Published: (2025)

Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning
by: Lee, Kichang, et al.
Published: (2024)

Mitigating Visual Hallucinations via Semantic Curriculum Preference Optimization in MLLMs
by: Li, Yuanshuai, et al.
Published: (2025)

Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
by: Fang, Zihan, et al.
Published: (2026)

OutSafe-Bench: A Benchmark for Multimodal Offensive Content Detection in Large Language Models
by: Yan, Yuping, et al.
Published: (2025)

Redefining Contributions: Shapley-Driven Federated Learning
by: Tastan, Nurbek, et al.
Published: (2024)

Multimodal Federated Learning in Healthcare: a Review
by: Thrasher, Jacob, et al.
Published: (2023)

Degree of Staleness-Aware Data Updating in Federated Learning
by: Liu, Tao, et al.
Published: (2025)