Copertina

Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sefidgaran, Milad, Chor, Romain, Zaidi, Abdellatif, Wan, Yijun
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Information Theory
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Heterogeneity Matters even More in Distributed Learning: Study from Generalization Perspective
di: Kavian, Masoud, et al.
Pubblicazione: (2025)

Data-dependent Generalization Bounds via Variable-Size Compressibility
di: Sefidgaran, Milad, et al.
Pubblicazione: (2023)

Minimal Communication-Cost Statistical Learning
di: Sefidgaran, Milad, et al.
Pubblicazione: (2024)

Minimum Description Length and Generalization Guarantees for Representation Learning
di: Sefidgaran, Milad, et al.
Pubblicazione: (2024)

Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors
di: Sefidgaran, Milad, et al.
Pubblicazione: (2025)

Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior
di: Sefidgaran, Milad, et al.
Pubblicazione: (2025)

Tighter CMI-Based Generalization Bounds via Stochastic Projection and Quantization
di: Sefidgaran, Milad, et al.
Pubblicazione: (2025)

A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning
di: Filatrella, Dario, et al.
Pubblicazione: (2026)

The Generalization Error of Supervised Machine Learning Algorithms
di: Perlaza, Samir M., et al.
Pubblicazione: (2024)

Learning Algorithm Generalization Error Bounds via Auxiliary Distributions
di: Aminian, Gholamali, et al.
Pubblicazione: (2022)

Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning
di: Wen, Dingzhu, et al.
Pubblicazione: (2025)

Tighter Information-Theoretic Generalization Bounds via a Novel Class of Change of Measure Inequalities
di: Liu, Yanxiao, et al.
Pubblicazione: (2026)

Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD
di: Wan, Yijun, et al.
Pubblicazione: (2023)

An Analysis of Minimum Error Entropy Loss Functions in Wireless Communications
di: Pallewela, Rumeshika, et al.
Pubblicazione: (2024)

Action-List Reinforcement Learning Syndrome Decoding for Binary Linear Block Codes
di: Taghipour, Milad, et al.
Pubblicazione: (2025)

Generalization in Federated Learning: A Conditional Mutual Information Framework
di: Wang, Ziqiao, et al.
Pubblicazione: (2025)

Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning
di: Barnes, L. P., et al.
Pubblicazione: (2022)

Pragmatic Goal-Oriented Communications under Semantic-Effectiveness Channel Errors
di: Hüttebräucker, Tomás, et al.
Pubblicazione: (2024)

Fast Rate Information-theoretic Bounds on Generalization Errors
di: Wu, Xuetong, et al.
Pubblicazione: (2023)

Information-Geometric Decomposition of Generalization Error in Unsupervised Learning
di: Kim, Gilhan
Pubblicazione: (2026)

Personalized Federated Learning for Generative AI-Assisted Semantic Communications
di: Peng, Yubo, et al.
Pubblicazione: (2024)

Generalization Error of Graph Neural Networks in the Mean-field Regime
di: Aminian, Gholamali, et al.
Pubblicazione: (2024)

Koopman-Based Generalization of Deep Reinforcement Learning With Application to Wireless Communications
di: Termehchi, Atefeh, et al.
Pubblicazione: (2025)

Load Balancing in Federated Learning
di: Javani, Alireza, et al.
Pubblicazione: (2024)

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

A General Error-Theoretical Analysis Framework for Constructing Compression Strategies
di: Zhang, Boyang, et al.
Pubblicazione: (2025)

On the Generalization for Transfer Learning: An Information-Theoretic Analysis
di: Wu, Xuetong, et al.
Pubblicazione: (2022)

Federated Dropout: Convergence Analysis and Resource Allocation
di: Xie, Sijing, et al.
Pubblicazione: (2024)

Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach
di: Abrar, Muhammad Faraz Ul, et al.
Pubblicazione: (2024)

Kalman Filter Aided Federated Koopman Learning
di: Chen, Yutao, et al.
Pubblicazione: (2025)

$r$Age-$k$: Communication-Efficient Federated Learning Using Age Factor
di: Mortaheb, Matin, et al.
Pubblicazione: (2024)

Communication-Learning Co-Design for Differentially Private Over-the-Air Federated Distillation
di: Hu, Zihao, et al.
Pubblicazione: (2025)

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

Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients
di: Wu, Zheshun, et al.
Pubblicazione: (2023)

A Hierarchical Federated Learning Approach for the Internet of Things
di: Azimi-Abarghouyi, Seyed Mohammad, et al.
Pubblicazione: (2024)

ProFL: Performative Robust Optimal Federated Learning
di: Zheng, Xue, et al.
Pubblicazione: (2024)

Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning
di: Ghaleb, Mustafa, et al.
Pubblicazione: (2025)

Balancing Client Participation in Federated Learning Using AoI
di: Javani, Alireza, et al.
Pubblicazione: (2025)

GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning
di: Peng, Yubo, et al.
Pubblicazione: (2024)

Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning
di: Jeong, Eunjeong, et al.
Pubblicazione: (2025)