Cover Image

Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Junbao, Xue, Jingfeng, Wang, Yong, Liu, Zhenyan, Huang, Lu
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Distributionally Robust Policy Learning under Concept Drifts
by: Wang, Jingyuan, et al.
Published: (2024)

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning
by: Peng, Fu, et al.
Published: (2025)

Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection
by: Zhang, Yiqun, et al.
Published: (2026)

Federated Learning with Profile Mapping under Distribution Shifts and Drifts
by: Li, Mohan, et al.
Published: (2026)

An Information-Theoretic Analysis for Federated Learning under Concept Drift
by: Peng, Fu, et al.
Published: (2025)

FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift
by: Zhang, Yong, et al.
Published: (2025)

Autonomous Concept Drift Threshold Determination
by: Lu, Pengqian, et al.
Published: (2025)

Online Boosting Adaptive Learning under Concept Drift for Multistream Classification
by: Yu, En, et al.
Published: (2023)

Generalized Incremental Learning under Concept Drift across Evolving Data Streams
by: Yu, En, et al.
Published: (2025)

Early Concept Drift Detection via Prediction Uncertainty
by: Lu, Pengqian, et al.
Published: (2024)

Distributionally Robust Federated Learning with Client Drift Minimization
by: Krouka, Mounssif, et al.
Published: (2025)

Federated Learning Clients Clustering with Adaptation to Data Drifts
by: Li, Minghao, et al.
Published: (2024)

Cluster Analysis and Concept Drift Detection in Malware
by: Mishra, Aniket, et al.
Published: (2025)

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
by: Wu, Xinghao, et al.
Published: (2024)

Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning
by: Salazar, Teresa, et al.
Published: (2024)

Distribution-Free Predictive Inference under Unknown Temporal Drift
by: Han, Elise, et al.
Published: (2024)

FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments
by: Mavromatis, Ioannis, et al.
Published: (2024)

Federated In-Context LLM Agent Learning
by: Wu, Panlong, et al.
Published: (2024)

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
by: McFadden, Shae, et al.
Published: (2025)

Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
by: Kalný, Tomáš, et al.
Published: (2026)

PID-Guided Partial Alignment for Multimodal Decentralized Federated Learning
by: Shi, Yanhang, et al.
Published: (2026)

FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts
by: Fenoglio, Dario, et al.
Published: (2025)

FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning
by: Zhou, Zhanting, et al.
Published: (2025)

DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning
by: Han, Yizhou, et al.
Published: (2026)

CORAL: Concept Drift Representation Learning for Co-evolving Time-series
by: Xu, Kunpeng, et al.
Published: (2025)

Learning under Distributional Drift: Prequential Reproducibility as an Intrinsic Statistical Resource
by: Zaichyk, Sofiya
Published: (2025)

Clustered Federated Learning via Embedding Distributions
by: Zhang, Dekai, et al.
Published: (2025)

Feature-Label Modal Alignment for Robust Partial Multi-Label Learning
by: Chen, Yu, et al.
Published: (2026)

STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2026)

Optimized Deep Learning Models for Malware Detection under Concept Drift
by: Maillet, William, et al.
Published: (2023)

Harnessing Feature Resonance under Arbitrary Target Alignment for Out-of-Distribution Node Detection
by: Yang, Shenzhi, et al.
Published: (2025)

Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
by: Wu, Junxiang, et al.
Published: (2026)

Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity
by: Chen, Xiaolu, et al.
Published: (2025)

Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks
by: Vaquet, Valerie, et al.
Published: (2024)

An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity
by: Pyeon, Junghee, et al.
Published: (2025)

Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
by: Fang, Zihan, et al.
Published: (2026)

CCFC++: Enhancing Federated Clustering through Feature Decorrelation
by: Yan, Jie, et al.
Published: (2024)

Time to Retrain? Detecting Concept Drifts in Machine Learning Systems
by: Pham, Tri Minh Triet, et al.
Published: (2024)

Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment
by: Ashrafee, Alif, et al.
Published: (2025)

Counterfactual Explanations Under Concept Drift
by: Kostrzewa, Marcin, et al.
Published: (2026)