The dramatic success of deep learning is largely due to the availability of data. Data samples are often acquired on edge devices, such as smartphones, vehicles, and sensors, and in some cases cannot be shared due to privacy considerations. Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. Learning in a federated manner differs from conventional centralized machine learning and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications. Consequently, dedicated schemes derived from these areas are expected to play an important role in the success of federated learning and the transition of deep learning from the domain of centralized servers to mobile edge devices.

中文翻译：

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联邦学习：信号处理视角

深度学习的巨大成功很大程度上归功于数据的可用性。数据样本通常是在智能手机、车辆和传感器等边缘设备上获取的，在某些情况下，出于隐私考虑而无法共享。联邦学习是一种新兴的机器学习范式，用于在多个边缘设备上训练模型，这些设备拥有本地数据集，而无需明确地交换数据。以联合方式学习不同于传统的集中式机器学习，并提出了几个核心独特的挑战和要求，这些挑战和要求与信号处理和通信领域研究的经典问题密切相关。最后，