Federated Sinkhorn
Fuente:
arXiv
Saved in:
| Main Authors: | Kulcsar, Jeremy, Kungurtsev, Vyacheslav, Korpas, Georgios, Giaconi, Giulio, Shoosmith, William |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging
by: Theologitis, Michail, et al.
Published: (2024)
by: Theologitis, Michail, et al.
Published: (2024)
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration
by: Morafah, Mahdi, et al.
Published: (2024)
by: Morafah, Mahdi, et al.
Published: (2024)
An Ensemble Scheme for Proactive Dominant Data Migration of Pervasive Tasks at the Edge
by: Boulougaris, Georgios, et al.
Published: (2024)
by: Boulougaris, Georgios, et al.
Published: (2024)
Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
by: Marfo, William, et al.
Published: (2025)
by: Marfo, William, et al.
Published: (2025)
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
by: Fenoglio, Dario, et al.
Published: (2024)
by: Fenoglio, Dario, et al.
Published: (2024)
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
by: Ma, Mengmeng, et al.
Published: (2024)
by: Ma, Mengmeng, et al.
Published: (2024)
Partial Federated Learning
by: Feng, Tiantian, et al.
Published: (2024)
by: Feng, Tiantian, et al.
Published: (2024)
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
by: Ji, Shaoxiong, et al.
Published: (2021)
by: Ji, Shaoxiong, et al.
Published: (2021)
Blockchain-Enabled Federated Learning
by: Rangwala, Murtaza, et al.
Published: (2025)
by: Rangwala, Murtaza, et al.
Published: (2025)
Federated K-means Clustering
by: Garst, Swier, et al.
Published: (2023)
by: Garst, Swier, et al.
Published: (2023)
Federated Automated Feature Engineering
by: Overman, Tom, et al.
Published: (2024)
by: Overman, Tom, et al.
Published: (2024)
Federated Frank-Wolfe Algorithm
by: Dadras, Ali, et al.
Published: (2024)
by: Dadras, Ali, et al.
Published: (2024)
Federated Temporal Graph Clustering
by: Zhou, Zihao, et al.
Published: (2024)
by: Zhou, Zihao, et al.
Published: (2024)
Communication-Efficient Federated Fine-Tuning
by: Theologitis, Michael, et al.
Published: (2025)
by: Theologitis, Michael, et al.
Published: (2025)
Federated Learning on Stochastic Neural Networks
by: Tang, Jingqiao, et al.
Published: (2025)
by: Tang, Jingqiao, et al.
Published: (2025)
Analytic Personalized Federated Meta-Learning
by: Gu, Shunxian, et al.
Published: (2025)
by: Gu, Shunxian, et al.
Published: (2025)
Orthogonal Calibration for Asynchronous Federated Learning
by: Zhang, Jiayun, et al.
Published: (2025)
by: Zhang, Jiayun, et al.
Published: (2025)
Federated Graph Learning with Graphless Clients
by: Fu, Xingbo, et al.
Published: (2024)
by: Fu, Xingbo, et al.
Published: (2024)
Photon: Federated LLM Pre-Training
by: Sani, Lorenzo, et al.
Published: (2024)
by: Sani, Lorenzo, et al.
Published: (2024)
Empowering Data Mesh with Federated Learning
by: Li, Haoyuan, et al.
Published: (2024)
by: Li, Haoyuan, et al.
Published: (2024)
Disentangling data distribution for Federated Learning
by: Zhao, Xinyuan, et al.
Published: (2024)
by: Zhao, Xinyuan, et al.
Published: (2024)
Federated LoRA with Sparse Communication
by: Kuo, Kevin, et al.
Published: (2024)
by: Kuo, Kevin, et al.
Published: (2024)
Balancing Similarity and Complementarity for Federated Learning
by: Yan, Kunda, et al.
Published: (2024)
by: Yan, Kunda, et al.
Published: (2024)
Queuing dynamics of asynchronous Federated Learning
by: Leconte, Louis, et al.
Published: (2024)
by: Leconte, Louis, et al.
Published: (2024)
Federated Learning over Connected Modes
by: Grinwald, Dennis, et al.
Published: (2024)
by: Grinwald, Dennis, et al.
Published: (2024)
Federated Learning based on Pruning and Recovery
by: Ma, Chengjie
Published: (2024)
by: Ma, Chengjie
Published: (2024)
Towards Client Driven Federated Learning
by: Li, Songze, et al.
Published: (2024)
by: Li, Songze, et al.
Published: (2024)
Communication Efficient and Provable Federated Unlearning
by: Tao, Youming, et al.
Published: (2024)
by: Tao, Youming, et al.
Published: (2024)
Review of Mathematical Optimization in Federated Learning
by: Yang, Shusen, et al.
Published: (2024)
by: Yang, Shusen, et al.
Published: (2024)
Centroid Approximation for Byzantine-Tolerant Federated Learning
by: Cambus, Mélanie, et al.
Published: (2025)
by: Cambus, Mélanie, et al.
Published: (2025)
orb-QFL: Orbital Quantum Federated Learning
by: Gurung, Dev, et al.
Published: (2025)
by: Gurung, Dev, et al.
Published: (2025)
Heterogeneous Federated Learning with Prototype Alignment and Upscaling
by: Lee, Gyuejeong, et al.
Published: (2025)
by: Lee, Gyuejeong, et al.
Published: (2025)
Hypernetworks for Model-Heterogeneous Personalized Federated Learning
by: Zhang, Chen, et al.
Published: (2025)
by: Zhang, Chen, et al.
Published: (2025)
Sketched Gaussian Mechanism for Private Federated Learning
by: Li, Qiaobo, et al.
Published: (2025)
by: Li, Qiaobo, et al.
Published: (2025)
Event-Driven Online Vertical Federated Learning
by: Wang, Ganyu, et al.
Published: (2025)
by: Wang, Ganyu, et al.
Published: (2025)
Collaborative Batch Size Optimization for Federated Learning
by: Geimer, Arno, et al.
Published: (2025)
by: Geimer, Arno, et al.
Published: (2025)
STHFL: Spatio-Temporal Heterogeneous Federated Learning
by: Guo, Shunxin, et al.
Published: (2025)
by: Guo, Shunxin, et al.
Published: (2025)
Rashomon Sets and Model Multiplicity in Federated Learning
by: Heilmann, Xenia, et al.
Published: (2026)
by: Heilmann, Xenia, et al.
Published: (2026)
Federated Learning in the Presence of Adversarial Client Unavailability
by: Su, Lili, et al.
Published: (2023)
by: Su, Lili, et al.
Published: (2023)
Flame: Simplifying Topology Extension in Federated Learning
by: Daga, Harshit, et al.
Published: (2023)
by: Daga, Harshit, et al.
Published: (2023)
Similar Items
-
Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging
by: Theologitis, Michail, et al.
Published: (2024) -
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration
by: Morafah, Mahdi, et al.
Published: (2024) -
An Ensemble Scheme for Proactive Dominant Data Migration of Pervasive Tasks at the Edge
by: Boulougaris, Georgios, et al.
Published: (2024) -
Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
by: Marfo, William, et al.
Published: (2025) -
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
by: Fenoglio, Dario, et al.
Published: (2024)