mlsanityData to 7 Oct 2026

From Federated Learning to Federated Neural Architecture Search: A Survey

arXiv 2009.05868 ↗First posted 12 Sep 2020cs.DCcited by 42

Not on the map. Why?

Abstract (arXiv)

Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern. Meanwhile, neural architecture search has become very popular in deep learning for automatically tuning the architecture and hyperparameters of deep neural networks. While both federated learning and neural architecture search are faced with many open challenges, searching for optimized neural architectures in the federated learning framework is particularly demanding. This survey paper starts with a brief introduction to federated learning, including both horizontal, vertical, and hybrid federated learning. Then, neural architecture search approaches based on reinforcement learning, evolutionary algorithms and gradient-based are presented. This is followed by a description of federated neural architecture search that has recently been proposed, which is categorized into online and offline implementations, and single- and multi-objective search approaches. Finally, remaining open research questions are outlined and promising research topics are suggested.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 8; 2023: 7; 2024: 8; 2025: 5; 2026: 3.

42 papers.

Sort:

By area:

  1. 2021Towards Personalized Federated Learningcited by 462
  2. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  3. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  4. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  5. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  6. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  7. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  8. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  9. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  10. 2023Federated Domain Generalization: A Surveycited by 25
  11. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  12. 2024A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trendscited by 16
30 more
  1. 2024Federated Distillation: A Surveycited by 14
  2. 2022A Survey on Computationally Efficient Neural Architecture Searchcited by 12
  3. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  4. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  5. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
  6. 2021A Federated Data-Driven Evolutionary Algorithmcited by 8
  7. 2021A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimizationcited by 5
  8. 2023Federated Deep Multi-View Clustering with Global Self-Supervisioncited by 5
  9. 2022Towards Fairness-Aware Multi-Objective Optimizationcited by 4
  10. 2022Differentially Private Vertical Federated Learningcited by 4
  11. 2022FEATHERS: Federated Architecture and Hyperparameter Searchcited by 3
  12. 2023Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systemscited by 3
  13. 2025Multi-objective methods in Federated Learning: A survey and taxonomycited by 3
  14. 2023Unlocking the Potential of Federated Learning for Deeper Modelscited by 1
  15. 2024Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect Predictioncited by 1
  16. 2026Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classificationcited by 1
  17. 2021A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trickcited by 0
  18. 2022Momentum Gradient Descent Federated Learning with Local Differential Privacycited by 0
  19. 2023Hierarchical and Decentralised Federated Learningcited by 0
  20. 2023Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitationscited by 0
  21. 2024DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learningcited by 0
  22. 2024Optimisation of federated learning settings under statistical heterogeneity variationscited by 0
  23. 2024Fair Differentiable Neural Network Architecture Search for Long-Tailed Data with Self-Supervised Learningcited by 0
  24. 2024DistDD: Distributed Data Distillation Aggregation through Gradient Matchingcited by 0
  25. 2025Principles and Components of Federated Learning Architecturescited by 0
  26. 2025Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Datacited by 0
  27. 2025DPFNAS: Differential Privacy-Enhanced Federated Neural Architecture Search for 6G Edge Intelligencecited by 0
  28. 2025A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Managementcited by 0
  29. 2026HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Searchcited by 0
  30. 2026Auto-FL-Research: Agentic Search for Federated Learning Algorithmscited by 0