Copertina

FedPop: Federated Population-based Hyperparameter Tuning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Haokun, Krompass, Denis, Gu, Jindong, Tresp, Volker
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
di: Chen, Haokun, et al.
Pubblicazione: (2024)

FedCore: Straggler-Free Federated Learning with Distributed Coresets
di: Guo, Hongpeng, et al.
Pubblicazione: (2024)

FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
di: Su, Shangchao, et al.
Pubblicazione: (2023)

FedAH: Aggregated Head for Personalized Federated Learning
di: Zhou, Pengzhan, et al.
Pubblicazione: (2024)

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
di: Zhang, Yuxin, et al.
Pubblicazione: (2024)

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning
di: Yoon, Taehwan, et al.
Pubblicazione: (2025)

FedMT: Federated Learning with Mixed-type Labels
di: Zhang, Qiong, et al.
Pubblicazione: (2022)

FedTLU: Federated Learning with Targeted Layer Updates
di: Park, Jong-Ik, et al.
Pubblicazione: (2024)

FedUV: Uniformity and Variance for Heterogeneous Federated Learning
di: Son, Ha Min, et al.
Pubblicazione: (2024)

FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources
di: Islam, Md Sirajul, et al.
Pubblicazione: (2026)

FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks
di: Lin, Zheng, et al.
Pubblicazione: (2023)

FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
di: Chen, Tan, et al.
Pubblicazione: (2025)

FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction
di: Li, Kaiyuan, et al.
Pubblicazione: (2024)

FedImpro: Measuring and Improving Client Update in Federated Learning
di: Tang, Zhenheng, et al.
Pubblicazione: (2024)

FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
di: Chen, Zihan, et al.
Pubblicazione: (2025)

FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
di: He, Jialuo, et al.
Pubblicazione: (2024)

SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
di: Zhang, Yuxin, et al.
Pubblicazione: (2024)

FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
di: Abbasi, Ali, et al.
Pubblicazione: (2024)

FedTrans: Efficient Federated Learning via Multi-Model Transformation
di: Zhu, Yuxuan, et al.
Pubblicazione: (2024)

FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning
di: Wang, Zihui, et al.
Pubblicazione: (2024)

CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics
di: Sharma, Harshit, et al.
Pubblicazione: (2024)

FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
di: Xu, Haotian, et al.
Pubblicazione: (2024)

GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
di: Seo, Jungwon, et al.
Pubblicazione: (2025)

FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
di: Islam, Md Sirajul, et al.
Pubblicazione: (2024)

FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
di: Jia, Yongzhe, et al.
Pubblicazione: (2024)

Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm
di: Elbakary, Ahmed, et al.
Pubblicazione: (2024)

Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
di: Zhao, Wanru, et al.
Pubblicazione: (2024)

CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
di: Wu, Huiwen, et al.
Pubblicazione: (2024)

FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
di: Pramanik, Anik, et al.
Pubblicazione: (2026)

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices
di: Yao, Dezhong, et al.
Pubblicazione: (2025)

A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
di: Chen, Minghui, et al.
Pubblicazione: (2025)

FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
di: Kahenga, Ferdinand, et al.
Pubblicazione: (2025)

FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity
di: Kahenga, Ferdinand, et al.
Pubblicazione: (2025)

FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
di: Jimenez-Gutierrez, Daniel M., et al.
Pubblicazione: (2026)

FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data
di: AbouNassar, Eman M., et al.
Pubblicazione: (2026)

FedFair^3: Unlocking Threefold Fairness in Federated Learning
di: Javaherian, Simin, et al.
Pubblicazione: (2024)

Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning
di: Singhal, Raghav, et al.
Pubblicazione: (2025)

Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
di: Wang, Shilong, et al.
Pubblicazione: (2025)

Learning Like Humans: Resource-Efficient Federated Fine-Tuning through Cognitive Developmental Stages
di: Wu, Yebo, et al.
Pubblicazione: (2025)

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
di: Ramesh, Hariharan, et al.
Pubblicazione: (2025)