Copertina

Dynamic Clustering in Federated Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kim, Yeongwoo, Hakim, Ezeddin Al, Haraldson, Johan, Eriksson, Henrik, Silva Jr., José Mairton B. da, Fischione, Carlo
Natura:	Preprint
Pubblicazione:	2020
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Blind Federated Learning via Over-the-Air q-QAM
di: Razavikia, Saeed, et al.
Pubblicazione: (2023)

Federated Learning Using Three-Operator ADMM
di: Kant, Shashi, et al.
Pubblicazione: (2022)

FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning
di: Mahmoudi, Afsaneh, et al.
Pubblicazione: (2022)

VecComp: Vector Computing via MIMO Digital Over-the-Air Computation
di: Razavikia, Saeed, et al.
Pubblicazione: (2025)

Decentralized Fairness Aware Multi Task Federated Learning for VR Network
di: Tharakan, Krishnendu S., et al.
Pubblicazione: (2025)

VR-VFL: Joint Rate and Client Selection for Vehicular Federated Learning Under Imperfect CSI
di: Karatas, Metehan, et al.
Pubblicazione: (2026)

A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees
di: Azimi-Abarghouyi, Seyed Mohammad, et al.
Pubblicazione: (2025)

Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
di: Azimi-Abarghouyi, Seyed Mohammad, et al.
Pubblicazione: (2025)

Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning
di: Song, Yeongwoo, et al.
Pubblicazione: (2022)

Hierarchical Federated Learning with SignSGD: A Highly Communication-Efficient Approach
di: Kazemi, Amirreza, et al.
Pubblicazione: (2026)

SumComp: Coding for Digital Over-the-Air Computation via the Ring of Integers
di: Razavikia, Saeed, et al.
Pubblicazione: (2023)

Uncovering Emergent Physics Representations Learned In-Context by Large Language Models
di: Song, Yeongwoo, et al.
Pubblicazione: (2025)

Stochastic Resetting Mitigates Latent Gradient Bias of SGD from Label Noise
di: Bae, Youngkyoung, et al.
Pubblicazione: (2024)

Machine Learning for Spectrum Sharing: A Survey
di: Guimarães, Francisco R. V., et al.
Pubblicazione: (2024)

Dynamic Clustering for Personalized Federated Learning on Heterogeneous Edge Devices
di: Liu, Heting, et al.
Pubblicazione: (2025)

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
di: Schönbauer, Sita, et al.
Pubblicazione: (2025)

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

Marginal Pseudo-Likelihood Learning of Markov Network structures
di: Pensar, Johan, et al.
Pubblicazione: (2014)

Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacy
di: Wei, Dongyu, et al.
Pubblicazione: (2025)

Comparative Evaluation of Clustered Federated Learning Methods
di: Ali, Michael Ben, et al.
Pubblicazione: (2024)

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

Cluster Specific Representation Learning
di: Sabanayagam, Mahalakshmi, et al.
Pubblicazione: (2024)

MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network
di: Fang, Kai, et al.
Pubblicazione: (2025)

Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
di: Bashir, Satwat, et al.
Pubblicazione: (2026)

Decoupled Subgraph Federated Learning
di: Aliakbari, Javad, et al.
Pubblicazione: (2024)

Interaction-Aware Gaussian Weighting for Clustered Federated Learning
di: Licciardi, Alessandro, et al.
Pubblicazione: (2025)

One-Shot Clustering for Federated Learning
di: Zuziak, Maciej Krzysztof, et al.
Pubblicazione: (2025)

Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching
di: Tharakan, Krishnendu S., et al.
Pubblicazione: (2025)

Language-Assisted Feature Transformation for Anomaly Detection
di: Yun, EungGu, et al.
Pubblicazione: (2025)

Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation
di: Wang, Jiaqi, et al.
Pubblicazione: (2024)

Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approach
di: Hamood, Moqbel, et al.
Pubblicazione: (2024)

Relaxed Contrastive Learning for Federated Learning
di: Seo, Seonguk, et al.
Pubblicazione: (2024)

Achieving Linear Speedup for Composite Federated Learning
di: Huang, Kun, et al.
Pubblicazione: (2026)

Differentially Private Federated Clustering with Random Rebalancing
di: Yang, Xiyuan, et al.
Pubblicazione: (2025)

The Fairness-Quality Trade-off in Clustering
di: Hakim, Rashida, et al.
Pubblicazione: (2024)

Distilling A Universal Expert from Clustered Federated Learning
di: Leng, Zeqi, et al.
Pubblicazione: (2025)

Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies
di: Guo, Yongxin, et al.
Pubblicazione: (2023)

A survey on Clustered Federated Learning: Taxonomy, Analysis and Applications
di: Ali, Michael Ben, et al.
Pubblicazione: (2025)

FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling
di: Chen, Cheng, et al.
Pubblicazione: (2020)

One-Shot Clustering for Federated Learning Under Clustering-Agnostic Assumption
di: Zuziak, Maciej Krzysztof, et al.
Pubblicazione: (2025)