Image de couverture de livre

ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Konstantin, Mirko, Fuchs, Moritz, Mukhopadhyay, Anirban
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

Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning
par: Konstantin, Mirko, et autres
Publié: (2025)

Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity
par: Babendererde, Niklas, et autres
Publié: (2025)

GAUDA: Generative Adaptive Uncertainty-guided Diffusion-based Augmentation for Surgical Segmentation
par: Frisch, Yannik, et autres
Publié: (2025)

Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning
par: Yashwanth, M, et autres
Publié: (2023)

ASMR: Activation-sharing Multi-resolution Coordinate Networks For Efficient Inference
par: Li, Jason Chun Lok, et autres
Publié: (2024)

Unsupervised Training of Neural Cellular Automata on Edge Devices
par: Kalkhof, John, et autres
Publié: (2024)

Rethinking Client-oriented Federated Graph Learning
par: Chen, Zekai, et autres
Publié: (2025)

Client Contribution Normalization for Enhanced Federated Learning
par: Kundalwal, Mayank Kumar, et autres
Publié: (2024)

Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing
par: Guo, Yongxin, et autres
Publié: (2024)

Distributionally Robust Federated Learning with Client Drift Minimization
par: Krouka, Mounssif, et autres
Publié: (2025)

Optimal Strategies for Federated Learning Maintaining Client Privacy
par: Bhaskar, Uday, et autres
Publié: (2025)

LEFL: Low Entropy Client Sampling in Federated Learning
par: Abebe, Waqwoya, et autres
Publié: (2023)

Communication-Efficient Federated Learning With Data and Client Heterogeneity
par: Zakerinia, Hossein, et autres
Publié: (2022)

Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
par: Xu, Jiahao, et autres
Publié: (2025)

fluke: Federated Learning Utility frameworK for Experimentation and research
par: Polato, Mirko
Publié: (2024)

Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification
par: Zhang, Boyang, et autres
Publié: (2024)

On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning
par: Chahoud, Mario, et autres
Publié: (2024)

Socially inspired Adaptive Coalition and Client Selection in Federated Learning
par: Licciardi, Alessandro, et autres
Publié: (2025)

Distribution-Controlled Client Selection to Improve Federated Learning Strategies
par: Düsing, Christoph, et autres
Publié: (2025)

Client Selection in Federated Learning with Data Heterogeneity and Network Latencies
par: Vardhan, Harsh, et autres
Publié: (2025)

FedDAPL: Toward Client-Private Generalization in Federated Learning
par: Loaliyan, Soroosh Safari, et autres
Publié: (2025)

Personalized One-shot Federated Graph Learning for Heterogeneous Clients
par: Yan, Guochen, et autres
Publié: (2024)

Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients
par: Ads, Mohamed, et autres
Publié: (2024)

Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learning
par: Jiang, Xiaorui, et autres
Publié: (2023)

Federated Learning with Extremely Noisy Clients via Negative Distillation
par: Lu, Yang, et autres
Publié: (2023)

Federated Graph Learning with Graphless Clients
par: Fu, Xingbo, et autres
Publié: (2024)

Towards Client Driven Federated Learning
par: Li, Songze, et autres
Publié: (2024)

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
par: Zhao, Boxin, et autres
Publié: (2021)

Federated Learning with Sample-level Client Drift Mitigation
par: Xu, Haoran, et autres
Publié: (2025)

Unlearning Clients, Features and Samples in Vertical Federated Learning
par: Varshney, Ayush K., et autres
Publié: (2025)

Federated Learning Clients Clustering with Adaptation to Data Drifts
par: Li, Minghao, et autres
Publié: (2024)

Communication-Efficient Federated Learning with Accelerated Client Gradient
par: Kim, Geeho, et autres
Publié: (2022)

Federated Granger Causality Learning for Interdependent Clients with State Space Representation
par: Mohanty, Ayush, et autres
Publié: (2025)

An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning
par: Chen, Yan-Ann, et autres
Publié: (2025)

ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning
par: Jin, Ruochen, et autres
Publié: (2025)

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions
par: Zakerinia, Hossein, et autres
Publié: (2025)

Client-supervised Federated Learning: Towards One-model-for-all Personalization
par: Yan, Peng, et autres
Publié: (2024)

Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
par: Allouah, Youssef, et autres
Publié: (2024)

Federated Learning in the Presence of Adversarial Client Unavailability
par: Su, Lili, et autres
Publié: (2023)

Debiasing Federated Learning with Correlated Client Participation
par: Sun, Zhenyu, et autres
Publié: (2024)