标题： 实时联邦进化神经架构搜索 显示英文标题

标题： Real-time Federated Evolutionary Neural Architecture Search

摘要： 联邦学习是一种分布式机器学习方法，用于隐私保护，但有两个主要技术挑战阻碍了联邦学习的更广泛应用。 一个是联邦学习对通信提出了高要求，因为必须在服务器和客户端之间传输大量模型参数。 另一个挑战是，在联邦学习中训练大型机器学习模型，如深度神经网络，需要大量的计算资源，这可能对边缘设备（如手机）来说不现实。 当在联邦学习中进行深度神经网络架构搜索时，问题会变得更加严重。 为了解决上述挑战，我们提出了一种实时联邦神经网络架构搜索的进化方法，该方法不仅优化模型性能，还减少了本地负载。 在搜索过程中，引入了一种双采样技术，其中对于每个个体，将主模型的一个随机采样的子模型发送到一组随机采样的客户端进行训练，而无需重新初始化。 这样，我们有效地降低了进化优化所需的计算和通信成本，并避免了本地模型性能的大幅波动，使所提出的框架非常适合实时联邦神经网络架构搜索。 显示英文摘要

摘要： 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.