论文标题
从联合学习到联合神经体系结构搜索：一项调查
From Federated Learning to Federated Neural Architecture Search: A Survey
论文作者
论文摘要
Federated Learning是最近提出的用于隐私保护的分布式机器学习范式，该范式发现了数据隐私是主要问题的广泛应用。同时，神经体系结构搜索在深度学习中非常流行，以自动调整深神经网络的体系结构和超参数。尽管联合学习和神经体系结构搜索都面临许多开放挑战，但在联合学习框架中寻找优化的神经体系结构尤其要求。该调查论文首先简要介绍了联合学习，包括水平，垂直和混合联合学习。然后，提出了基于强化学习，进化算法和基于梯度的神经体系结构搜索方法。接下来是对最近提出的联合神经体系结构搜索的描述，该搜索已归类为在线和离线实现，以及单目标搜索方法。最后，概述了剩余的开放研究问题，并提出了有希望的研究主题。
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.
