On Noisy Evaluation in Federated Hyperparameter Tuning

Type: Preprint

Publication Date: 2022-01-01

Citations: 0

DOI: https://doi.org/10.48550/arxiv.2212.08930

Locations

  • arXiv (Cornell University) - View - PDF
  • DataCite API - View

Similar Works

Action Title Year Authors
+ Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective. 2021 Huanle Zhang
Mi Zhang
Xin Liu
Prasant Mohapatra
Michael DeLucia
+ Federated Learning Hyper-Parameter Tuning from a System Perspective 2022 Huanle Zhang
Lei Fu
Mi Zhang
Pengfei Hu
Xiuzhen Cheng
Prasant Mohapatra
Xin Liu
+ PDF Chat Federated Learning Hyperparameter Tuning From a System Perspective 2023 Huanle Zhang
Lei Fu
Mi Zhang
Pengfei Hu
Xiuzhen Cheng
Prasant Mohapatra
Xin Liu
+ Motley: Benchmarking Heterogeneity and Personalization in Federated Learning 2022 Shanshan Wu
Tian Li
Zachary Charles
Xiao Yu
Ziyu Liu
Zheng Xu
Virginia Smith
+ PDF Chat Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism 2024 Yasaman Saadati
M. Hadi Amini
+ Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning 2022 John Nguyen
Kshitiz Malik
Maziar Sanjabi
Michael Rabbat
+ A Comprehensive Survey On Client Selections in Federated Learning 2023 Ala Gouissem
Zina Chkirbene
Ridha Hamila
+ A comprehensive survey on client selections in federated learning 2024 Ala Gouissem
Zina Chkirbene
Ridha Hamila
+ Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning 2022 John Nguyen
Jianyu Wang
Kshitiz Malik
Maziar Sanjabi
Michael Rabbat
+ PDF Chat FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective 2022 Huanle Zhang
Mi Zhang
Xin Liu
Prasant Mohapatra
Michael DeLucia
+ On Large-Cohort Training for Federated Learning 2021 Zachary Charles
Zachary Garrett
Zhouyuan Huo
Sergei Shmulyian
Virginia Smith
+ On Large-Cohort Training for Federated Learning 2021 Zachary Charles
Zachary Garrett
Zhouyuan Huo
Sergei Shmulyian
Virginia Smith
+ On Large-Cohort Training for Federated Learning 2021 Zachary Charles
Zachary Garrett
Zhouyuan Huo
Sergei Shmulyian
Virginia Smith
+ Personalized Federated Learning using Hypernetworks 2021 Aviv Shamsian
Aviv Navon
Ethan Fetaya
Gal Chechik
+ The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness 2024 Mengyao Du
Miao Zhang
Yuwen Pu
Kai Xu
Shouling Ji
Quanjun Yin
+ PDF Chat Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials 2024 Jonathan Scott
Áine Cahill
+ PDF Chat FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning 2024 Nurbek Tastan
Samuel Horváth
Martin Takáč
Karthik Nandakumar
+ PDF Chat How to Privately Tune Hyperparameters in Federated Learning? Insights from a Benchmark Study 2024 Natalija Mitic
Apostolos Pyrgelis
Sinem Sav
+ Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations 2023 Torsten Krauß
Alexandra Dmitrienko
+ Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing 2021 Mikhail Khodak
Renbo Tu
Li Tian
Liam Li
Maria-Florina Balcan
Virginia Smith
Ameet Talwalkar

Works That Cite This (0)

Action Title Year Authors

Works Cited by This (0)

Action Title Year Authors