mlsanityData to 7 Oct 2026

FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective

arXiv 2110.03061 ↗First posted 6 Oct 2021cs.LGcited by 7

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Abstract (arXiv)

Federated learning (FL) hyper-parameters significantly affect the training overheads in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters puts a high burden on FL practitioners since various applications prefer different training preferences. In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements of FL training. FedTune is lightweight and flexible, achieving 8.48%-26.75% improvement for different datasets compared to fixed FL hyper-parameters.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

18 of 40 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2020Adaptive Federated Optimizationcited by 1,036
  6. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  7. 2019Towards Federated Learning at Scale: System Designcited by 826
  8. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 741
  9. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
  10. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  11. 2021A Field Guide to Federated Optimizationcited by 312
  12. 2019A Survey on Distributed Machine Learningcited by 187
6 more
  1. 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 181
  2. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  3. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 84
  4. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  5. 2019Intelligent Traffic Monitoring Systems for Vehicle Classification: A Surveycited by 10
  6. 2020Deep Learning in the Era of Edge Computing: Challenges and Opportunitiescited by 10

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Cited by year

Citing papers per year: 2022: 5; 2023: 1; 2024: 1.

7 papers.

  1. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  2. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  3. 2022FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimizationcited by 6
  4. 2022On Noisy Evaluation in Federated Hyperparameter Tuningcited by 6
  5. 2022Federated Learning Hyper-Parameter Tuning from a System Perspectivecited by 1
  6. 2023FedPop: Federated Population-based Hyperparameter Tuningcited by 1
  7. 2024DistDD: Distributed Data Distillation Aggregation through Gradient Matchingcited by 0