Cover Image

FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Yuxi, Luo, Guibo, Zhu, Yuesheng
Format:	Preprint
Published:	2024
Subjects:	Image and Video Processing Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing I.4.6; I.2.11
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ADF-LoRA: Alternating Low-Rank Aggregation for Decentralized Federated Fine-Tuning
by: Wang, Xiaoyu, et al.
Published: (2025)

Hierarchical Training of Deep Neural Networks Using Early Exiting
by: Sepehri, Yamin, et al.
Published: (2023)

Asynchronous Byzantine Federated Learning
by: Cox, Bart, et al.
Published: (2024)

Training Diffusion Models with Federated Learning
by: de Goede, Matthijs, et al.
Published: (2024)

Aergia: Leveraging Heterogeneity in Federated Learning Systems
by: Cox, Bart, et al.
Published: (2022)

Parameterizing Federated Continual Learning for Reproducible Research
by: Cox, Bart, et al.
Published: (2024)

Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
by: Zuo, Yuncong, et al.
Published: (2024)

AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments
by: Zhang, Guilin, et al.
Published: (2025)

Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning
by: Zhang, Haoran, et al.
Published: (2025)

Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism
by: Saadati, Yasaman, et al.
Published: (2024)

FedPLT: Scalable, Resource-Efficient, and Heterogeneity-Aware Federated Learning via Partial Layer Training
by: Dabaja, Ahmad, et al.
Published: (2026)

Towards Building Private LLMs: Exploring Multi-Node Expert Parallelism on Apple Silicon for Mixture-of-Experts Large Language Model
by: Chen, Mu-Chi, et al.
Published: (2025)

Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
by: Lan, Guangchen, et al.
Published: (2024)

Foam Segmentation in Wastewater Treatment Plants: A Federated Learning Approach with Segment Anything Model 2
by: Duman, Mehmet Batuhan, et al.
Published: (2025)

SPARK: Igniting Communication-Efficient Decentralized Learning via Stage-wise Projected NTK and Accelerated Regularization
by: Xia, Li
Published: (2025)

Training LLMs on HPC Systems: Best Practices from the OpenGPT-X Project
by: Penke, Carolin, et al.
Published: (2025)

A Segmentation Foundation Model for Diverse-type Tumors
by: Xie, Jianhao, et al.
Published: (2024)

Roadmap for Edge AI: A Dagstuhl Perspective
by: Ding, Aaron Yi, et al.
Published: (2021)

Connecting Large Language Model Agent to High Performance Computing Resource
by: Ma, Heng, et al.
Published: (2025)

Quantize Once, Train Fast: Allreduce-Compatible Compression with Provable Guarantees
by: Xin, Jihao, et al.
Published: (2023)

Comparison of Autoscaling Frameworks for Containerised Machine-Learning-Applications in a Local and Cloud Environment
by: Schroeder, Christian, et al.
Published: (2023)

SI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data Sharing
by: Zhao, Mingjie, et al.
Published: (2026)

XFED: Non-Collusive Model Poisoning Attack Against Byzantine-Robust Federated Classifiers
by: Mouri, Israt Jahan, et al.
Published: (2026)

TAGC: Optimizing Gradient Communication in Distributed Transformer Training
by: Polyakov, Igor, et al.
Published: (2025)

Federated Learning with Differential Privacy
by: Banse, Adrien, et al.
Published: (2024)

Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation
by: Orozco, Fermin, et al.
Published: (2024)

DAGER: Exact Gradient Inversion for Large Language Models
by: Petrov, Ivo, et al.
Published: (2024)

SPEAR:Exact Gradient Inversion of Batches in Federated Learning
by: Dimitrov, Dimitar I., et al.
Published: (2024)

A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments
by: Colybes, Elouan, et al.
Published: (2026)

PriPHiT: Privacy-Preserving Hierarchical Training of Deep Neural Networks
by: Sepehri, Yamin, et al.
Published: (2024)

A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
by: Rosendal, Daan, et al.
Published: (2026)

SparkAttention: High-Performance Multi-Head Attention for Large Models on Volta GPU Architecture
by: Xu, Youxuan, et al.
Published: (2025)

AutoDDL: Automatic Distributed Deep Learning with Near-Optimal Bandwidth Cost
by: Chen, Jinfan, et al.
Published: (2023)

How Machine Learning-Data Driven Replication Strategies Enhance Fault Tolerance in Large-Scale Distributed Systems
by: Murimi, Almond Kiruthu
Published: (2025)

Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodes
by: Motta, Steven, et al.
Published: (2026)

CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation
by: Pugachev, Sergey
Published: (2025)

Separating Intelligence from Execution: A Workflow Engine for the Model Context Protocol
by: Parmar, Abhinav Singh
Published: (2026)

A Survey on Efficient Federated Learning Methods for Foundation Model Training
by: Woisetschläger, Herbert, et al.
Published: (2024)

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training
by: Feng, Wenjiao, et al.
Published: (2025)

FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning
by: Haque, Md Rafid, et al.
Published: (2025)