Saved in:
| Main Authors: | Shadin, Nazmus Shakib, Cummings, Aaron, Zhang, Xinyue, Deng, Bobin |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2606.01607 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Modeling Electric Vehicle Car-Following Behavior: Classical vs Machine Learning Approach
by: Uddin, Md. Shihab, et al.
Published: (2025)
by: Uddin, Md. Shihab, et al.
Published: (2025)
FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation
by: Jiang, Wenzheng, et al.
Published: (2025)
by: Jiang, Wenzheng, et al.
Published: (2025)
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
by: Li, Yichen, et al.
Published: (2025)
by: Li, Yichen, et al.
Published: (2025)
FedKDX: Federated Learning with Negative Knowledge Distillation for Enhanced Healthcare AI Systems
by: Pham, Quang-Tu, et al.
Published: (2026)
by: Pham, Quang-Tu, et al.
Published: (2026)
FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective
by: Zhou, Zhekai, et al.
Published: (2025)
by: Zhou, Zhekai, et al.
Published: (2025)
FedEL: Federated Elastic Learning for Heterogeneous Devices
by: Zhang, Letian, et al.
Published: (2025)
by: Zhang, Letian, et al.
Published: (2025)
Robust Knowledge Distillation Based on Feature Variance Against Backdoored Teacher Model
by: Chen, Jinyin, et al.
Published: (2024)
by: Chen, Jinyin, et al.
Published: (2024)
FedSCAM (Federated Sharpness-Aware Minimization with Clustered Aggregation and Modulation): Scam-resistant SAM for Robust Federated Optimization in Heterogeneous Environments
by: Rahil, Sameer, et al.
Published: (2025)
by: Rahil, Sameer, et al.
Published: (2025)
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
by: Zhu, Yinlin, et al.
Published: (2024)
by: Zhu, Yinlin, et al.
Published: (2024)
FedSDR: Federated Self-Distillation with Rectification
by: Ren, Ziheng, et al.
Published: (2026)
by: Ren, Ziheng, et al.
Published: (2026)
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
by: Ji, Xinyuan, et al.
Published: (2024)
by: Ji, Xinyuan, 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)
FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification
by: Weng, Ziqiao, et al.
Published: (2025)
by: Weng, Ziqiao, et al.
Published: (2025)
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning
by: Kim, Seongyoon, et al.
Published: (2024)
by: Kim, Seongyoon, et al.
Published: (2024)
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity
by: Qiao, Yu, et al.
Published: (2024)
by: Qiao, Yu, et al.
Published: (2024)
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)
Model Merging via Multi-Teacher Knowledge Distillation
by: Dalili, Seyed Arshan, et al.
Published: (2025)
by: Dalili, Seyed Arshan, et al.
Published: (2025)
Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors
by: Fang, Luyang, et al.
Published: (2026)
by: Fang, Luyang, et al.
Published: (2026)
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
by: Shen, Leming, et al.
Published: (2025)
by: Shen, Leming, et al.
Published: (2025)
FedUV: Uniformity and Variance for Heterogeneous Federated Learning
by: Son, Ha Min, et al.
Published: (2024)
by: Son, Ha Min, et al.
Published: (2024)
BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoring
by: Shu, Zixuan, et al.
Published: (2026)
by: Shu, Zixuan, et al.
Published: (2026)
FedD2S: Personalized Data-Free Federated Knowledge Distillation
by: Atapour, Kawa, et al.
Published: (2024)
by: Atapour, Kawa, et al.
Published: (2024)
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning
by: Nguyen, Manh V., et al.
Published: (2024)
by: Nguyen, Manh V., 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)
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities
by: Liu, Yi, et al.
Published: (2025)
by: Liu, Yi, et al.
Published: (2025)
LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments
by: Sun, Pengcheng, et al.
Published: (2025)
by: Sun, Pengcheng, et al.
Published: (2025)
FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity
by: Sun, Zhaobin, et al.
Published: (2024)
by: Sun, Zhaobin, et al.
Published: (2024)
FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments
by: Balija, Sree Bhargavi
Published: (2025)
by: Balija, Sree Bhargavi
Published: (2025)
Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments
by: Sun, Pengcheng, et al.
Published: (2025)
by: Sun, Pengcheng, et al.
Published: (2025)
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer
by: Gao, Xin, et al.
Published: (2024)
by: Gao, Xin, et al.
Published: (2024)
FedRecon: Missing Modality Reconstruction in Heterogeneous Distributed Environments
by: Liu, Junming, et al.
Published: (2025)
by: Liu, Junming, et al.
Published: (2025)
FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation
by: Tan, Min, et al.
Published: (2026)
by: Tan, Min, et al.
Published: (2026)
Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents
by: Chen, Xiang, et al.
Published: (2025)
by: Chen, Xiang, et al.
Published: (2025)
FedZMG: Efficient Client-Side Optimization in Federated Learning
by: Zantalis, Fotios, et al.
Published: (2026)
by: Zantalis, Fotios, et al.
Published: (2026)
Federated Learning on Virtual Heterogeneous Data with Local-global Distillation
by: Huang, Chun-Yin, et al.
Published: (2023)
by: Huang, Chun-Yin, et al.
Published: (2023)
FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection
by: Németh, Gergely D., et al.
Published: (2025)
by: Németh, Gergely D., et al.
Published: (2025)
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
by: Peng, Zihao, et al.
Published: (2025)
by: Peng, Zihao, et al.
Published: (2025)
FedSlate:A Federated Deep Reinforcement Learning Recommender System
by: Deng, Yongxin, et al.
Published: (2024)
by: Deng, Yongxin, et al.
Published: (2024)
FedCoT: Federated Chain-of-Thought Distillation for Large Language Models
by: Fan, Tao, et al.
Published: (2024)
by: Fan, Tao, 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)
Similar Items
-
Modeling Electric Vehicle Car-Following Behavior: Classical vs Machine Learning Approach
by: Uddin, Md. Shihab, et al.
Published: (2025) -
FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation
by: Jiang, Wenzheng, et al.
Published: (2025) -
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
by: Li, Yichen, et al.
Published: (2025) -
FedKDX: Federated Learning with Negative Knowledge Distillation for Enhanced Healthcare AI Systems
by: Pham, Quang-Tu, et al.
Published: (2026) -
FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective
by: Zhou, Zhekai, et al.
Published: (2025)