Analysis of regularized federated learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Liu, Langming, Zhou, Dingxuan |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Non-convex composite federated learning with heterogeneous data
por: Zhang, Jiaojiao, et al.
Publicado: (2025)
por: Zhang, Jiaojiao, et al.
Publicado: (2025)
Version age-based client scheduling policy for federated learning
por: Hu, Xinyi, et al.
Publicado: (2024)
por: Hu, Xinyi, et al.
Publicado: (2024)
Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles
por: Cha, Narisu, et al.
Publicado: (2024)
por: Cha, Narisu, et al.
Publicado: (2024)
Role-Aware Multi-modal federated learning system for detecting phishing webpages
por: Wang, Bo, et al.
Publicado: (2025)
por: Wang, Bo, et al.
Publicado: (2025)
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network
por: Jang, Jaeyeon, et al.
Publicado: (2024)
por: Jang, Jaeyeon, et al.
Publicado: (2024)
Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data
por: Salmeron, Jose L., et al.
Publicado: (2024)
por: Salmeron, Jose L., et al.
Publicado: (2024)
Consensus learning: A novel decentralised ensemble learning paradigm
por: Magureanu, Horia, et al.
Publicado: (2024)
por: Magureanu, Horia, et al.
Publicado: (2024)
Convergence Analysis of Split Federated Learning on Heterogeneous Data
por: Han, Pengchao, et al.
Publicado: (2024)
por: Han, Pengchao, et al.
Publicado: (2024)
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
por: Crawshaw, Michael, et al.
Publicado: (2024)
por: Crawshaw, Michael, et al.
Publicado: (2024)
Understanding Stragglers in Large Model Training Using What-if Analysis
por: Lin, Jinkun, et al.
Publicado: (2025)
por: Lin, Jinkun, et al.
Publicado: (2025)
GraphStorm: all-in-one graph machine learning framework for industry applications
por: Zheng, Da, et al.
Publicado: (2024)
por: Zheng, Da, et al.
Publicado: (2024)
Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
por: Shanmugavelu, Sanjif, et al.
Publicado: (2024)
por: Shanmugavelu, Sanjif, et al.
Publicado: (2024)
Federated Temporal Graph Clustering
por: Zhou, Zihao, et al.
Publicado: (2024)
por: Zhou, Zihao, et al.
Publicado: (2024)
Towards Sustainable Large Language Model Serving
por: Nguyen, Sophia, et al.
Publicado: (2024)
por: Nguyen, Sophia, et al.
Publicado: (2024)
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT
por: Wang, Heqiang, et al.
Publicado: (2024)
por: Wang, Heqiang, et al.
Publicado: (2024)
Convergence Analysis of Decentralized ASGD
por: Tosi, Mauro DL, et al.
Publicado: (2023)
por: Tosi, Mauro DL, et al.
Publicado: (2023)
DHP: Efficient Scaling of MLLM Training with Dynamic Hybrid Parallelism
por: Niu, Yifan, et al.
Publicado: (2026)
por: Niu, Yifan, et al.
Publicado: (2026)
Stabilizing Decentralized Federated Fine-Tuning via Topology-Aware Alternating LoRA
por: Wang, Xiaoyu, et al.
Publicado: (2026)
por: Wang, Xiaoyu, et al.
Publicado: (2026)
Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning
por: Li, Qingming, et al.
Publicado: (2024)
por: Li, Qingming, et al.
Publicado: (2024)
Machine Learning for Consistency Violation Faults Analysis
por: Giri, Kamal, et al.
Publicado: (2025)
por: Giri, Kamal, et al.
Publicado: (2025)
A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network
por: Chen, Feiyi, et al.
Publicado: (2023)
por: Chen, Feiyi, et al.
Publicado: (2023)
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity
por: Li, Bo, et al.
Publicado: (2023)
por: Li, Bo, et al.
Publicado: (2023)
Fog enabled distributed training architecture for federated learning
por: Kumar, Aditya, et al.
Publicado: (2024)
por: Kumar, Aditya, et al.
Publicado: (2024)
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
por: Zhou, Liuzhi, et al.
Publicado: (2024)
por: Zhou, Liuzhi, et al.
Publicado: (2024)
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
por: Shao, Yumeng, et al.
Publicado: (2024)
por: Shao, Yumeng, et al.
Publicado: (2024)
An Information-Theoretic Analysis for Federated Learning under Concept Drift
por: Peng, Fu, et al.
Publicado: (2025)
por: Peng, Fu, et al.
Publicado: (2025)
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
por: Chellapandi, Vishnu Pandi, et al.
Publicado: (2024)
por: Chellapandi, Vishnu Pandi, et al.
Publicado: (2024)
Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis
por: Liang, Yipeng, et al.
Publicado: (2024)
por: Liang, Yipeng, et al.
Publicado: (2024)
Injecting Adrenaline into LLM Serving: Boosting Resource Utilization and Throughput via Attention Disaggregation
por: Liang, Yunkai, et al.
Publicado: (2025)
por: Liang, Yunkai, et al.
Publicado: (2025)
Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding
por: Sima, Jin, et al.
Publicado: (2024)
por: Sima, Jin, et al.
Publicado: (2024)
Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
por: Ye, Haoxiang, et al.
Publicado: (2025)
por: Ye, Haoxiang, et al.
Publicado: (2025)
Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification
por: Papanikolaou, Athanasios, et al.
Publicado: (2026)
por: Papanikolaou, Athanasios, et al.
Publicado: (2026)
Privacy-preserving quantum federated learning via gradient hiding
por: Li, Changhao, et al.
Publicado: (2023)
por: Li, Changhao, et al.
Publicado: (2023)
Integrated user scheduling and beam steering in over-the-air federated learning for mobile IoT
por: Liu, Shengheng, et al.
Publicado: (2025)
por: Liu, Shengheng, et al.
Publicado: (2025)
A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement
por: Vargis, Tom Richard, et al.
Publicado: (2024)
por: Vargis, Tom Richard, et al.
Publicado: (2024)
Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data
por: Li, Bangyu, et al.
Publicado: (2024)
por: Li, Bangyu, et al.
Publicado: (2024)
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
por: Jiang, Chutian, et al.
Publicado: (2024)
por: Jiang, Chutian, et al.
Publicado: (2024)
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
por: Qin, Laiqiao, et al.
Publicado: (2024)
por: Qin, Laiqiao, et al.
Publicado: (2024)
Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
por: Zhao, Lingxiao, et al.
Publicado: (2025)
por: Zhao, Lingxiao, et al.
Publicado: (2025)
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
por: Zhou, Zihao, et al.
Publicado: (2025)
por: Zhou, Zihao, et al.
Publicado: (2025)
Ejemplares similares
-
Non-convex composite federated learning with heterogeneous data
por: Zhang, Jiaojiao, et al.
Publicado: (2025) -
Version age-based client scheduling policy for federated learning
por: Hu, Xinyi, et al.
Publicado: (2024) -
Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles
por: Cha, Narisu, et al.
Publicado: (2024) -
Role-Aware Multi-modal federated learning system for detecting phishing webpages
por: Wang, Bo, et al.
Publicado: (2025) -
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network
por: Jang, Jaeyeon, et al.
Publicado: (2024)