Copertina

Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Marfo, William, Tosh, Deepak K., Moore, Shirley V.
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Efficient Client Selection in Federated Learning
di: Marfo, William, et al.
Pubblicazione: (2025)

Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems
di: Marfo, William, et al.
Pubblicazione: (2025)

Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
di: Marfo, William, et al.
Pubblicazione: (2025)

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning
di: Marfo, William, et al.
Pubblicazione: (2024)

Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection
di: HaghighiFard, M. Saeid, et al.
Pubblicazione: (2024)

Network Anomaly Detection in Distributed Edge Computing Infrastructure
di: Marfo, William, et al.
Pubblicazione: (2025)

Unlearning Clients, Features and Samples in Vertical Federated Learning
di: Varshney, Ayush K., et al.
Pubblicazione: (2025)

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection
di: Fei, Qinjun, et al.
Pubblicazione: (2025)

Detecting Atypical Clients in Federated Learning via Representation-Level Divergence
di: Pérez-Corral, Cristian, et al.
Pubblicazione: (2026)

Client-Centric Federated Adaptive Optimization
di: Sun, Jianhui, et al.
Pubblicazione: (2025)

SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation
di: Zhong, Luying, et al.
Pubblicazione: (2024)

An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks
di: Tajgardan, Mohsen, et al.
Pubblicazione: (2026)

FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection
di: Németh, Gergely D., et al.
Pubblicazione: (2025)

Anomaly Detection in Electric Vehicle Charging Stations Using Federated Learning
di: C, Bishal K, et al.
Pubblicazione: (2025)

Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client
di: Xia, Jun, et al.
Pubblicazione: (2024)

VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systems
di: Lakas, Mohamed, et al.
Pubblicazione: (2026)

Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding
di: Wilhelm, Patrick, et al.
Pubblicazione: (2026)

BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks
di: Yadav, Sushilkumar, et al.
Pubblicazione: (2024)

Optimizing Federated Learning by Entropy-Based Client Selection
di: Lutz, Andreas, et al.
Pubblicazione: (2024)

Federated Learning with Sample-level Client Drift Mitigation
di: Xu, Haoran, et al.
Pubblicazione: (2025)

Communication-Efficient Federated Learning with Accelerated Client Gradient
di: Kim, Geeho, et al.
Pubblicazione: (2022)

FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
di: Shen, Leming, et al.
Pubblicazione: (2025)

Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
di: Weng, Shudi, et al.
Pubblicazione: (2025)

Submodular Maximization Approaches for Equitable Client Selection in Federated Learning
di: Jiménez, Andrés Catalino Castillo, et al.
Pubblicazione: (2024)

FedZMG: Efficient Client-Side Optimization in Federated Learning
di: Zantalis, Fotios, et al.
Pubblicazione: (2026)

An Interpretable Client Decision Tree Aggregation process for Federated Learning
di: Argente-Garrido, Alberto, et al.
Pubblicazione: (2024)

Tackling Selfish Clients in Federated Learning
di: Augello, Andrea, et al.
Pubblicazione: (2024)

Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
di: Ranaweera, Kanishka, et al.
Pubblicazione: (2025)

FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
di: Wen, Tian, et al.
Pubblicazione: (2026)

Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
di: Jiang, Xuefeng, et al.
Pubblicazione: (2024)

FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
di: Wang, Xinpeng, et al.
Pubblicazione: (2024)

Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approach
di: Tan, Jinghong, et al.
Pubblicazione: (2025)

Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks
di: Chaudhary, Devashish, et al.
Pubblicazione: (2026)

Federated Linear Contextual Bandits with Heterogeneous Clients
di: Blaser, Ethan, et al.
Pubblicazione: (2024)

SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients
di: Shen, Zhikang, et al.
Pubblicazione: (2026)

Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation
di: Behfar, Stefan, et al.
Pubblicazione: (2026)

NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients
di: Kang, Honggu, et al.
Pubblicazione: (2023)

Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series
di: Chen, Kuan-Cheng, et al.
Pubblicazione: (2025)

PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
di: Li, Liangyan, et al.
Pubblicazione: (2025)

Multi-Head Spectral-Adaptive Graph Anomaly Detection
di: Cao, Qingyue, et al.
Pubblicazione: (2025)