Imagen de Portada

PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhang, Tianyi, Zhang, Shirui, Chen, Ziwei, Liu, Dianbo
Formato:	Preprint
Publicado:	2021
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos
por: Zhang, Tianyi, et al.
Publicado: (2024)

A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications
por: Cremonesi, Francesco, et al.
Publicado: (2024)

Balance of Number of Embedding and their Dimensions in Vector Quantization
por: Chen, Hang, et al.
Publicado: (2024)

Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models
por: Wang, Zhibiao, et al.
Publicado: (2025)

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

Discrete Messages Improve Communication Efficiency among Isolated Intelligent Agents
por: Chen, Hang, et al.
Publicado: (2023)

Multi-task Federated Learning with Encoder-Decoder Structure: Enabling Collaborative Learning Across Different Tasks
por: Zhou, Jingxuan, et al.
Publicado: (2025)

Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment
por: Verhelst, Théo, et al.
Publicado: (2022)

Expected Return Causes Outcome-Level Mode Collapse in Reinforcement Learning and How to Fix It with Inverse Probability Scaling
por: Sinha, Abhijeet, et al.
Publicado: (2026)

Multi-Level Additive Modeling for Structured Non-IID Federated Learning
por: Chen, Shutong, et al.
Publicado: (2024)

Knowledge-aware contrastive heterogeneous molecular graph learning
por: Chen, Mukun, et al.
Publicado: (2025)

Parameter Efficient Multi-task Model Fusion with Partial Linearization
por: Tang, Anke, et al.
Publicado: (2023)

SwiftTS: A Swift Selection Framework for Time Series Pre-trained Models via Multi-task Meta-Learning
por: Zhang, Tengxue, et al.
Publicado: (2025)

ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
por: Gong, Zirui, et al.
Publicado: (2026)

ARC: A Generalist Graph Anomaly Detector with In-Context Learning
por: Liu, Yixin, et al.
Publicado: (2024)

PID-Guided Partial Alignment for Multimodal Decentralized Federated Learning
por: Shi, Yanhang, et al.
Publicado: (2026)

Bridging Mechanistic Interpretability and Prompt Engineering with Gradient Ascent for Interpretable Persona Control
por: Saini, Harshvardhan, et al.
Publicado: (2026)

Decentralized Federated Learning by Partial Message Exchange
por: Sha, Shan, et al.
Publicado: (2026)

FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
por: Zhao, Yunfeng, et al.
Publicado: (2026)

Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity
por: Zhang, Handi, et al.
Publicado: (2024)

Partial Federated Learning
por: Feng, Tiantian, et al.
Publicado: (2024)

Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
por: Gong, Zirui, et al.
Publicado: (2025)

Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
por: Fan, Ziqing, et al.
Publicado: (2024)

FedMerge: Federated Personalization via Model Merging
por: Chen, Shutong, et al.
Publicado: (2025)

Evaluating Federated Learning approaches for mammography under breast density heterogeneity
por: Quintana, Gonzalo Iñaki, et al.
Publicado: (2026)

FedAPM: Federated Learning via ADMM with Partial Model Personalization
por: Zhu, Shengkun, et al.
Publicado: (2025)

Developing robust methods to handle missing data in real-world applications effectively
por: Zhou, Youran, et al.
Publicado: (2025)

Why Go Full? Elevating Federated Learning Through Partial Network Updates
por: Wang, Haolin, et al.
Publicado: (2024)

ROG$_{PL}$: Robust Open-Set Graph Learning via Region-Based Prototype Learning
por: Zhang, Qin, et al.
Publicado: (2024)

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
por: Ji, Shaoxiong, et al.
Publicado: (2021)

ColNet: Collaborative Optimization in Decentralized Federated Multi-task Learning Systems
por: Feng, Chao, et al.
Publicado: (2025)

Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning
por: Gan, Xiaoyu, et al.
Publicado: (2024)

A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning
por: Zhang, Xiaojin, et al.
Publicado: (2023)

Evolution Guided Generative Flow Networks
por: Ikram, Zarif, et al.
Publicado: (2024)

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

FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
por: Liang, Han, et al.
Publicado: (2024)

BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks
por: Chen, Gongping, et al.
Publicado: (2023)

Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping
por: Fan, Ziqing, et al.
Publicado: (2024)

Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
por: Yang, Tong, et al.
Publicado: (2023)

Provable Partially Observable Reinforcement Learning with Privileged Information
por: Cai, Yang, et al.
Publicado: (2024)