# From Federated Learning to Federated Neural Architecture Search: A Survey

arXiv: https://arxiv.org/abs/2009.05868 · HTML: https://mlsanity.com/paper/2009.05868/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2020.

## Built on by (most cited first, 30 of 42)

- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [Communication Efficiency in Federated Learning: Achievements and Challenges](https://mlsanity.com/paper/2107.10996/) (2021)
- [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://mlsanity.com/paper/2102.12920/) (2021)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [Vertical Federated Learning: A Structured Literature Review](https://mlsanity.com/paper/2212.00622/) (2022)
- [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024)
- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [A Survey on Computationally Efficient Neural Architecture Search](https://mlsanity.com/paper/2206.01520/) (2022)
- [A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things](https://mlsanity.com/paper/2104.10501/) (2021)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [A Federated Data-Driven Evolutionary Algorithm](https://mlsanity.com/paper/2102.08288/) (2021)
- [A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization](https://mlsanity.com/paper/2106.12086/) (2021)
- [Federated Deep Multi-View Clustering with Global Self-Supervision](https://mlsanity.com/paper/2309.13697/) (2023)
- [Towards Fairness-Aware Multi-Objective Optimization](https://mlsanity.com/paper/2207.12138/) (2022)
- [Differentially Private Vertical Federated Learning](https://mlsanity.com/paper/2211.06782/) (2022)
- [FEATHERS: Federated Architecture and Hyperparameter Search](https://mlsanity.com/paper/2206.12342/) (2022)
- [Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems](https://mlsanity.com/paper/2305.07135/) (2023)
- [Multi-objective methods in Federated Learning: A survey and taxonomy](https://mlsanity.com/paper/2502.03108/) (2025)
- [Unlocking the Potential of Federated Learning for Deeper Models](https://mlsanity.com/paper/2306.02701/) (2023)
- [DistDD: Distributed Data Distillation Aggregation through Gradient Matching](https://mlsanity.com/paper/2410.08665/) (2024)
- [Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect Prediction](https://mlsanity.com/paper/2412.17317/) (2024)
- [Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification](https://mlsanity.com/paper/2605.08121/) (2026)
- [A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick](https://mlsanity.com/paper/2110.15277/) (2021)

## 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.