Abstract
Federated Neural Architecture Search (FedNAS) aims to automate model design for privacy-preserving Federated Learning (FL) but currently faces two critical bottlenecks: unguided supernet training that yields suboptimal models, and costly multi-hour pipelines for post-training subnet discovery. We introduce DeepFedNAS, a novel, two-phase framework underpinned by a principled, multi-objective fitness function that synthesizes mathematical network design with architectural heuristics. Enabled by a re-engineered supernet, DeepFedNAS introduces Federated Pareto Optimal Supernet Training, which leverages a pre-computed Pareto-optimal cache of high-fitness architectures as an intelligent curriculum to optimize shared supernet weights. Subsequently, its Predictor-Free Search Method eliminates the need for costly accuracy surrogates by utilizing this fitness function as a direct, zero-cost proxy for accuracy, enabling on-demand subnet discovery in mere seconds. DeepFedNAS achieves state-of-the-art accuracy (e.g., up to 1.21% absolute improvement on CIFAR-100), superior parameter and communication efficiency, and a substantial ~61x speedup in total post-training search pipeline time. By reducing the pipeline from over 20 hours to approximately 20 minutes (including initial cache generation) and enabling 20-second individual subnet searches, DeepFedNAS makes hardware-aware FL deployments instantaneous and practical. The complete source code and experimental scripts are available at: this https URL
Abstract (translated)
联邦神经架构搜索（FedNAS）旨在为隐私保护的联邦学习（FL）自动化模型设计，但目前面临两个关键瓶颈：缺乏指导的超网训练导致次优模型产生，以及耗时数小时的后训练子网络发现流程。我们提出了DeepFedNAS，这是一个新颖的两阶段框架，基于一个原则性的多目标适应性函数，将数学网络设计与架构启发式相结合。借助重新设计的超网，DeepFedNAS引入了联邦帕累托最优超网训练方法，该方法利用预计算的高适应性架构帕累托缓存作为智能课程表来优化共享超网权重。随后，其无需预测器搜索方法通过使用此适应性函数直接替代精度代理（零成本），从而消除昂贵的精度代理需求，实现按需子网络发现仅需几秒即可完成。 DeepFedNAS实现了最先进的准确性（例如，在CIFAR-100上高达1.21%绝对改进）、卓越的参数和通信效率，并且在总后训练搜索流水线时间上提高了约61倍的速度。通过将流程从超过20小时缩短至大约20分钟（包括初始缓存生成）并实现每个子网络仅需20秒的单独搜索，DeepFedNAS使硬件感知FL部署即时和实用化。 完整的源代码和实验脚本可在以下网址获取：[此URL]
URL
https://arxiv.org/abs/2601.15127
