Clustered Federated Learning with Hierarchical Knowledge Distillation
Fuente:
arXiv
Saved in:
| Main Authors: | Ahmad, Sabtain, Kanatbekova, Meerzhan, Brandic, Ivona, Aral, Atakan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing
by: De Maio, Vincenzo, et al.
Published: (2024)
by: De Maio, Vincenzo, et al.
Published: (2024)
Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers
by: Zhang, Yue, et al.
Published: (2026)
by: Zhang, Yue, et al.
Published: (2026)
Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
by: Sun, Pengcheng, et al.
Published: (2024)
by: Sun, Pengcheng, et al.
Published: (2024)
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
by: Chadha, Mohak, et al.
Published: (2024)
by: Chadha, Mohak, et al.
Published: (2024)
Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case
by: Herbst, Sabrina, et al.
Published: (2024)
by: Herbst, Sabrina, et al.
Published: (2024)
On the Byzantine-Resilience of Distillation-Based Federated Learning
by: Roux, Christophe, et al.
Published: (2024)
by: Roux, Christophe, et al.
Published: (2024)
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks
by: Chen, Tan, et al.
Published: (2024)
by: Chen, Tan, et al.
Published: (2024)
LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2025)
by: Zhang, Yuxin, et al.
Published: (2025)
Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
by: Lee, Seunghyun, et al.
Published: (2025)
by: Lee, Seunghyun, et al.
Published: (2025)
ABBA-VSM: Time Series Classification using Symbolic Representation on the Edge
by: Kanatbekova, Meerzhan, et al.
Published: (2024)
by: Kanatbekova, Meerzhan, et al.
Published: (2024)
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2024)
by: Zhang, Yuxin, et al.
Published: (2024)
Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
by: Abouaomar, Anas, et al.
Published: (2025)
by: Abouaomar, Anas, et al.
Published: (2025)
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
by: Gao, Zhidong, et al.
Published: (2024)
by: Gao, Zhidong, et al.
Published: (2024)
DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge
by: May, Daniel, et al.
Published: (2024)
by: May, Daniel, et al.
Published: (2024)
Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity
by: Wang, Zheng, et al.
Published: (2024)
by: Wang, Zheng, et al.
Published: (2024)
FLStore: Efficient Federated Learning Storage for non-training workloads
by: Khan, Ahmad Faraz, et al.
Published: (2025)
by: Khan, Ahmad Faraz, et al.
Published: (2025)
DecHW: Heterogeneous Decentralized Federated Learning Exploiting Second-Order Information
by: Ahmad, Adnan, et al.
Published: (2026)
by: Ahmad, Adnan, et al.
Published: (2026)
FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
by: Pramanik, Anik, et al.
Published: (2026)
by: Pramanik, Anik, et al.
Published: (2026)
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)
by: Islam, Md Sirajul, et al.
Published: (2024)
CA-AFP: Cluster-Aware Adaptive Federated Pruning
by: Jha, Om Govind, et al.
Published: (2026)
by: Jha, Om Govind, et al.
Published: (2026)
FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity
by: Kahenga, Ferdinand, et al.
Published: (2025)
by: Kahenga, Ferdinand, et al.
Published: (2025)
FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)
Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments
by: Liu, Junming, et al.
Published: (2025)
by: Liu, Junming, et al.
Published: (2025)
Federated Multi-Objective Learning
by: Yang, Haibo, et al.
Published: (2023)
by: Yang, Haibo, et al.
Published: (2023)
Federated Learning with Flexible Architectures
by: Park, Jong-Ik, et al.
Published: (2024)
by: Park, Jong-Ik, et al.
Published: (2024)
FedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated Learning
by: Reguieg, Hamza, et al.
Published: (2026)
by: Reguieg, Hamza, et al.
Published: (2026)
On Using Large-Batches in Federated Learning
by: Tyagi, Sahil
Published: (2025)
by: Tyagi, Sahil
Published: (2025)
Uncertainty-Aware Explainable Federated Learning
by: Zhang, Yanci, et al.
Published: (2025)
by: Zhang, Yanci, et al.
Published: (2025)
Efficient Client Selection in Federated Learning
by: Marfo, William, et al.
Published: (2025)
by: Marfo, William, et al.
Published: (2025)
DiReDi: Distillation and Reverse Distillation for AIoT Applications
by: Sun, Chen, et al.
Published: (2024)
by: Sun, Chen, et al.
Published: (2024)
Tackling Selfish Clients in Federated Learning
by: Augello, Andrea, et al.
Published: (2024)
by: Augello, Andrea, et al.
Published: (2024)
DFML: Decentralized Federated Mutual Learning
by: Khalil, Yasser H., et al.
Published: (2024)
by: Khalil, Yasser H., et al.
Published: (2024)
Federated Learning with Limited Node Labels
by: Tang, Bisheng, et al.
Published: (2024)
by: Tang, Bisheng, et al.
Published: (2024)
Influence-oriented Personalized Federated Learning
by: Tan, Yue, et al.
Published: (2024)
by: Tan, Yue, et al.
Published: (2024)
Variational Bayes for Federated Continual Learning
by: Yao, Dezhong, et al.
Published: (2024)
by: Yao, Dezhong, et al.
Published: (2024)
Efficient Federated Learning with Timely Update Dissemination
by: Jia, Juncheng, et al.
Published: (2025)
by: Jia, Juncheng, et al.
Published: (2025)
Privacy in Federated Learning with Spiking Neural Networks
by: Aksu, Dogukan, et al.
Published: (2025)
by: Aksu, Dogukan, et al.
Published: (2025)
Gradient Correction in Federated Learning with Adaptive Optimization
by: Chen, Evan, et al.
Published: (2025)
by: Chen, Evan, et al.
Published: (2025)
Efficient Asynchronous Federated Learning with Sparsification and Quantization
by: Jia, Juncheng, et al.
Published: (2023)
by: Jia, Juncheng, et al.
Published: (2023)
Federated Graph Learning with Structure Proxy Alignment
by: Fu, Xingbo, et al.
Published: (2024)
by: Fu, Xingbo, et al.
Published: (2024)
Similar Items
-
Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing
by: De Maio, Vincenzo, et al.
Published: (2024) -
Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers
by: Zhang, Yue, et al.
Published: (2026) -
Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
by: Sun, Pengcheng, et al.
Published: (2024) -
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
by: Chadha, Mohak, et al.
Published: (2024) -
Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case
by: Herbst, Sabrina, et al.
Published: (2024)