Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Kainuma, Haruki, Nishio, Takayuki |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments
by: Okada, Yuya, et al.
Published: (2025)
by: Okada, Yuya, et al.
Published: (2025)
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
by: Hu, Gangqiang, et al.
Published: (2024)
by: Hu, Gangqiang, et al.
Published: (2024)
PFedDST: Personalized Federated Learning with Decentralized Selection Training
by: Fan, Mengchen, et al.
Published: (2025)
by: Fan, Mengchen, et al.
Published: (2025)
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training
by: Soltany, Milad, et al.
Published: (2024)
by: Soltany, Milad, et al.
Published: (2024)
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences
by: Feng, Chao, et al.
Published: (2025)
by: Feng, Chao, 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)
by: Liu, Yuze, et al.
Published: (2025)
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme
by: Long, Qianyu, et al.
Published: (2024)
by: Long, Qianyu, et al.
Published: (2024)
$Λ$-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI
by: Ohta, Shoki, et al.
Published: (2023)
by: Ohta, Shoki, et al.
Published: (2023)
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
by: Huang, Chun-Yin, et al.
Published: (2024)
by: Huang, Chun-Yin, et al.
Published: (2024)
Byzantine-Robust Aggregation for Securing Decentralized Federated Learning
by: Cajaraville-Aboy, Diego, et al.
Published: (2024)
by: Cajaraville-Aboy, Diego, et al.
Published: (2024)
From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning
by: Feng, Chao, et al.
Published: (2025)
by: Feng, Chao, et al.
Published: (2025)
UA-PDFL: A Personalized Approach for Decentralized Federated Learning
by: Zhu, Hangyu, et al.
Published: (2024)
by: Zhu, Hangyu, et al.
Published: (2024)
Fairness-Aware Job Scheduling for Multi-Job Federated Learning
by: Shi, Yuxin, et al.
Published: (2024)
by: Shi, Yuxin, et al.
Published: (2024)
Tram-FL: Routing-based Model Training for Decentralized Federated Learning
by: Maejima, Kota, et al.
Published: (2023)
by: Maejima, Kota, et al.
Published: (2023)
On the Limits of Momentum in Decentralized and Federated Optimization
by: Zaccone, Riccardo, et al.
Published: (2025)
by: Zaccone, Riccardo, et al.
Published: (2025)
Fair Concurrent Training of Multiple Models in Federated Learning
by: Siew, Marie, et al.
Published: (2024)
by: Siew, Marie, et al.
Published: (2024)
SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling
by: Zhang, Yiqi, et al.
Published: (2026)
by: Zhang, Yiqi, et al.
Published: (2026)
FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated Learning
by: Park, Sanghyeon, et al.
Published: (2025)
by: Park, Sanghyeon, et al.
Published: (2025)
From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks
by: Salihovic, Allan, et al.
Published: (2025)
by: Salihovic, Allan, et al.
Published: (2025)
MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning
by: Duchesne, Michael, et al.
Published: (2024)
by: Duchesne, Michael, et al.
Published: (2024)
Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing
by: Kai, Haruki, et al.
Published: (2025)
by: Kai, Haruki, et al.
Published: (2025)
Decentralized Adversarial Training over Graphs
by: Cao, Ying, et al.
Published: (2023)
by: Cao, Ying, et al.
Published: (2023)
Online Decentralized Federated Multi-task Learning With Trustworthiness in Cyber-Physical Systems
by: Odeyomi, Olusola, et al.
Published: (2025)
by: Odeyomi, Olusola, et al.
Published: (2025)
Centralized vs Decentralized Federated Learning: A trade-off performance analysis
by: Medjadji, Chaimaa, et al.
Published: (2026)
by: Medjadji, Chaimaa, et al.
Published: (2026)
Trust and Resilience in Federated Learning Through Smart Contracts Enabled Decentralized Systems
by: Cassano, Lorenzo, et al.
Published: (2024)
by: Cassano, Lorenzo, et al.
Published: (2024)
BlazeFL: Fast and Deterministic Federated Learning Simulation
by: Azuma, Kitsuya, et al.
Published: (2026)
by: Azuma, Kitsuya, et al.
Published: (2026)
Bayesian Federated Learning for Continual Training
by: Milasheuski, Usevalad, et al.
Published: (2025)
by: Milasheuski, Usevalad, et al.
Published: (2025)
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement
by: Li, Qinglun, et al.
Published: (2024)
by: Li, Qinglun, et al.
Published: (2024)
Decentralized Federated Learning: A Survey on Security and Privacy
by: Hallaji, Ehsan, et al.
Published: (2024)
by: Hallaji, Ehsan, et al.
Published: (2024)
ScheduleFree+: Scaling Learning-Rate-Free & Schedule-Free Learning to Large Language Models
by: Defazio, Aaron
Published: (2026)
by: Defazio, Aaron
Published: (2026)
Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler
by: Dremov, Aleksandr, et al.
Published: (2025)
by: Dremov, Aleksandr, et al.
Published: (2025)
Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning
by: Wang, Tongxi, et al.
Published: (2026)
by: Wang, Tongxi, et al.
Published: (2026)
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
by: Xie, Luyuan, et al.
Published: (2025)
by: Xie, Luyuan, et al.
Published: (2025)
Exploring Federated Learning for Thermal Urban Feature Segmentation -- A Comparison of Centralized and Decentralized Approaches
by: Duda, Leonhard, et al.
Published: (2025)
by: Duda, Leonhard, et al.
Published: (2025)
Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach
by: Piao, Chengzhe, et al.
Published: (2024)
by: Piao, Chengzhe, et al.
Published: (2024)
Intrinsic Training Signals for Federated Learning Aggregation
by: Fiorini, Cosimo, et al.
Published: (2025)
by: Fiorini, Cosimo, et al.
Published: (2025)
Split Federated Learning Architectures for High-Accuracy and Low-Delay Model Training
by: Papageorgiou, Yiannis, et al.
Published: (2026)
by: Papageorgiou, Yiannis, et al.
Published: (2026)
Zero-Shot Decentralized Federated Learning
by: Masano, Alessio, et al.
Published: (2025)
by: Masano, Alessio, et al.
Published: (2025)
DFML: Decentralized Federated Mutual Learning
by: Khalil, Yasser H., et al.
Published: (2024)
by: Khalil, Yasser H., et al.
Published: (2024)
MAGNET: Autonomous Expert Model Generation via Decentralized Autoresearch and BitNet Training
by: Kim, Yongwan, et al.
Published: (2026)
by: Kim, Yongwan, et al.
Published: (2026)
Similar Items
-
SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments
by: Okada, Yuya, et al.
Published: (2025) -
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
by: Hu, Gangqiang, et al.
Published: (2024) -
PFedDST: Personalized Federated Learning with Decentralized Selection Training
by: Fan, Mengchen, et al.
Published: (2025) -
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training
by: Soltany, Milad, et al.
Published: (2024) -
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences
by: Feng, Chao, et al.
Published: (2025)