# Real-time Federated Evolutionary Neural Architecture Search

arXiv: https://arxiv.org/abs/2003.02793 · HTML: https://mlsanity.com/paper/2003.02793/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 20 of 20)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [SPIDER: Searching Personalized Neural Architecture for Federated Learning](https://mlsanity.com/paper/2112.13939/) (2021)
- [A Survey on Computationally Efficient Neural Architecture Search](https://mlsanity.com/paper/2206.01520/) (2022)
- [Self-supervised Cross-silo Federated Neural Architecture Search](https://mlsanity.com/paper/2101.11896/) (2021)
- [Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures](https://mlsanity.com/paper/2107.08111/) (2021)
- [A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends](https://mlsanity.com/paper/2209.06399/) (2022)
- [EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2311.07485/) (2023)
- [Towards Fairness-Aware Multi-Objective Optimization](https://mlsanity.com/paper/2207.12138/) (2022)
- [Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification](https://mlsanity.com/paper/2209.13233/) (2022)
- [Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures](https://mlsanity.com/paper/2207.05321/) (2022)
- [Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks](https://mlsanity.com/paper/2304.10749/) (2023)
- [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)
- [Federated Loss Exploration for Improved Convergence on Non-IID Data](https://mlsanity.com/paper/2506.18640/) (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)
- [A Genetic Algorithm-Based Approach for Automated Optimization of Kolmogorov-Arnold Networks in Classification Tasks](https://mlsanity.com/paper/2501.17411/) (2025)
- [FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferences](https://mlsanity.com/paper/2210.08450/) (2022)
- [A Survey on Multi-Objective Neural Architecture Search](https://mlsanity.com/paper/2307.09099/) (2023)
- [DPFNAS: Differential Privacy-Enhanced Federated Neural Architecture Search for 6G Edge Intelligence](https://mlsanity.com/paper/2509.23030/) (2025)

## Abstract (arXiv)

Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning. One is that federated learning raises high demands on communication, since a large number of model parameters must be transmitted between the server and the clients. The other challenge is that training large machine learning models such as deep neural networks in federated learning requires a large amount of computational resources, which may be unrealistic for edge devices such as mobile phones. The problem becomes worse when deep neural architecture search is to be carried out in federated learning. To address the above challenges, we propose an evolutionary approach to real-time federated neural architecture search that not only optimize the model performance but also reduces the local payload. During the search, a double-sampling technique is introduced, in which for each individual, a randomly sampled sub-model of a master model is transmitted to a number of randomly sampled clients for training without reinitialization. This way, we effectively reduce computational and communication costs required for evolutionary optimization and avoid big performance fluctuations of the local models, making the proposed framework well suited for real-time federated neural architecture search.