Image de couverture de livre

FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Fenoglio, Dario, Li, Mohan, Barbiero, Pietro, Lane, Nicholas D., Langheinrich, Marc, Gjoreski, Martin
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Federated Learning with Profile Mapping under Distribution Shifts and Drifts
par: Li, Mohan, et autres
Publié: (2026)

Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
par: Fenoglio, Dario, et autres
Publié: (2024)

A Survey on Federated Learning in Human Sensing
par: Li, Mohan, et autres
Publié: (2025)

ERIS: Enhancing Privacy and Scalability in Federated Learning via Federated Shard Aggregation
par: Fenoglio, Dario, et autres
Publié: (2026)

Counterfactual Explanations for Clustering Models
par: Spagnol, Aurora, et autres
Publié: (2024)

Federated Concept-Based Models: Interpretable models with distributed supervision
par: Fenoglio, Dario, et autres
Publié: (2026)

Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors
par: Fenoglio, Dario, et autres
Publié: (2025)

Counterfactual Concept Bottleneck Models
par: Dominici, Gabriele, et autres
Publié: (2024)

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
par: Dominici, Gabriele, et autres
Publié: (2024)

AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
par: Dominici, Gabriele, et autres
Publié: (2024)

A Survey on Decentralized Federated Learning
par: Gabrielli, Edoardo, et autres
Publié: (2023)

Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
par: Ezzeddine, Fatima, et autres
Publié: (2024)

Sheaf HyperNetworks for Personalized Federated Learning
par: Nguyen, Bao, et autres
Publié: (2024)

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts
par: Zarlenga, Mateo Espinosa, et autres
Publié: (2025)

Bringing Federated Learning to Space
par: Kim, Grace, et autres
Publié: (2025)

FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
par: Guo, Yongxin, et autres
Publié: (2023)

Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts
par: Najafi, Amir, et autres
Publié: (2024)

Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation
par: Sovrano, Francesco, et autres
Publié: (2026)

Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
par: Duan, Moming, et autres
Publié: (2021)

FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
par: You, Runze, et autres
Publié: (2025)

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning
par: Debot, David, et autres
Publié: (2025)

Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift
par: Park, Heewon, et autres
Publié: (2026)

Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift
par: Chen, Junbao, et autres
Publié: (2024)

Trustworthy Machine Learning under Distribution Shifts
par: Huang, Zhuo
Publié: (2025)

Ordered Local Momentum for Asynchronous Distributed Learning under Arbitrary Delays
par: Shi, Chang-Wei, et autres
Publié: (2026)

Clustered Federated Learning via Embedding Distributions
par: Zhang, Dekai, et autres
Publié: (2025)

Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations
par: Kim, Grace, et autres
Publié: (2024)

FLAT: Latent-Driven Arbitrary-Target Backdoor Attacks in Federated Learning
par: Nguyen, Tuan, et autres
Publié: (2025)

Adaptive Estimation and Learning under Temporal Distribution Shift
par: Baby, Dheeraj, et autres
Publié: (2025)

In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks
par: Sovrano, Francesco, et autres
Publié: (2026)

Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts
par: Marinova, Matea, et autres
Publié: (2026)

Tolerant Algorithms for Learning with Arbitrary Covariate Shift
par: Goel, Surbhi, et autres
Publié: (2024)

Towards Transparent Reasoning: What Drives Faithfulness in Large Language Models?
par: McMillan, Teague, et autres
Publié: (2025)

A study on performance limitations in Federated Learning
par: Mohan, Karthik
Publié: (2025)

Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
par: Goel, Surbhi, et autres
Publié: (2026)

ShiftKD: Benchmarking Knowledge Distillation under Distribution Shift
par: Zhang, Songming, et autres
Publié: (2023)

An Enhanced Federated Prototype Learning Method under Domain Shift
par: Kuang, Liang, et autres
Publié: (2024)

1.58-bit FLUX
par: Yang, Chenglin, et autres
Publié: (2024)

Efficient Discrepancy Testing for Learning with Distribution Shift
par: Chandrasekaran, Gautam, et autres
Publié: (2024)

Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection
par: Zhang, Yiqun, et autres
Publié: (2026)