mlsanityData to 7 Oct 2026

FEATHERS: Federated Architecture and Hyperparameter Search

arXiv 2206.12342 ↗First posted 24 Jun 2022cs.LGcited by 3

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Abstract (arXiv)

Deep neural architectures have profound impact on achieved performance in many of today's AI tasks, yet, their design still heavily relies on human prior knowledge and experience. Neural architecture search (NAS) together with hyperparameter optimization (HO) helps to reduce this dependence. However, state of the art NAS and HO rapidly become infeasible with increasing amount of data being stored in a distributed fashion, typically violating data privacy regulations such as GDPR and CCPA. As a remedy, we introduce FEATHERS - FEderated ArchiTecture and HypERparameter Search, a method that not only optimizes both neural architectures and optimization-related hyperparameters jointly in distributed data settings, but further adheres to data privacy through the use of differential privacy (DP). We show that FEATHERS efficiently optimizes architectural and optimization-related hyperparameters alike, while demonstrating convergence on classification tasks at no detriment to model performance when complying with privacy constraints.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 25 of its 32 references that are in the map. First posted on arXiv 24 Jun 2022.

Builds on

25 of 32 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  5. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  7. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  8. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  9. 2018SNAS: Stochastic Neural Architecture Searchcited by 288
  10. 2019Searching for A Robust Neural Architecture in Four GPU Hourscited by 258
  11. 2021Enhanced Membership Inference Attacks against Machine Learning Modelscited by 237
  12. 2016Transfer Learning for Low-Resource Neural Machine Translationcited by 188
13 more
  1. 2017Genetic CNNcited by 124
  2. 2019SGAS: Sequential Greedy Architecture Searchcited by 69
  3. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  4. 2020MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulationcited by 56
  5. 2020From Federated Learning to Federated Neural Architecture Search: A Surveycited by 42
  6. 2020Neuroevolution in Deep Neural Networks: Current Trends and Future Challengescited by 33
  7. 2021Differentially Private Federated Bayesian Optimization with Distributed Explorationcited by 32
  8. 2020Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Searchcited by 30
  9. 2019Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameterscited by 16
  10. 2021FLoRA: Single-shot Hyper-parameter Optimization for Federated Learningcited by 14
  11. 2020Differentially-private Federated Neural Architecture Searchcited by 9
  12. 2018Learning Rate Adaptation for Federated and Differentially Private Learningcited by 7
  13. 2021Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learningcited by 6

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filteringcited by 3
  2. 2024Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanismcited by 1
  3. 2025DP-Hype: Federated Differentially Private Hyperparameter Searchcited by 0