论文标题
使用神经体系结构搜索的资源感知的异质联合学习
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search
论文作者
论文摘要
联合学习（FL）广泛用于培训在分布式和隐私设置中的AI/ML模型。 FL系统中的参与者边缘设备通常包含非独立和相同分布的（非IID）私人数据和不均分布的计算资源。在异质联合网络中优化AI/ML模型的同时，保留用户数据隐私需要我们解决数据和系统/资源异质性。为了应对这些挑战，我们提出了资源感知的联合学习（RAFL）。 RAFL使用神经体系结构搜索（NAS）将资源感知的专业模型分配给边缘设备，并允许通过知识提取和融合来部署异质模型体系结构。组合NAS和FL可以为资源多样性边缘设备的按需定制模型部署。此外，我们提出了一种多模型体系结构融合方案，允许汇总分布式学习结果。结果表明，与SOTA相比，RAFL的资源效率卓越。
Federated Learning (FL) is extensively used to train AI/ML models in distributed and privacy-preserving settings. Participant edge devices in FL systems typically contain non-independent and identically distributed (Non-IID) private data and unevenly distributed computational resources. Preserving user data privacy while optimizing AI/ML models in a heterogeneous federated network requires us to address data and system/resource heterogeneity. To address these challenges, we propose Resource-aware Federated Learning (RaFL). RaFL allocates resource-aware specialized models to edge devices using Neural Architecture Search (NAS) and allows heterogeneous model architecture deployment by knowledge extraction and fusion. Combining NAS and FL enables on-demand customized model deployment for resource-diverse edge devices. Furthermore, we propose a multi-model architecture fusion scheme allowing the aggregation of the distributed learning results. Results demonstrate RaFL's superior resource efficiency compared to SoTA.
