The stringent requirements for low-latency and privacy of the emerging
high-stake applications with intelligent devices such as drones and smart
vehicles make the cloud computing inapplicable in these scenarios. Instead,
edge machine learning becomes increasingly attractive for performing training
and inference directly at network edges without sending data to a centralized
data center. This stimulates a nascent field termed as federated learning for
training a machine learning model on computation, storage, energy and bandwidth
limited mobile devices in a distributed manner. To preserve data privacy and
address the issues of unbalanced and non-IID data points across different
devices, the federated averaging algorithm has been proposed for global model
aggregation by computing the weighted average of locally updated model at each
selected device. However, the limited communication bandwidth becomes the main
bottleneck for aggregating the locally computed updates. We thus propose a
novel over-the-air computation based approach for fast global model aggregation
via exploring the superposition property of a wireless multiple-access channel.
This is achieved by joint device selection and beamforming design, which is
modeled as a sparse and low-rank optimization problem to support efficient
algorithms design. To achieve this goal, we provide a
difference-of-convex-functions (DC) representation for the sparse and low-rank
function to enhance sparsity and accurately detect the fixed-rank constraint in
the procedure of device selection. A DC algorithm is further developed to solve
the resulting DC program with global convergence guarantees. The algorithmic
advantages and admirable performance of the proposed methodologies are
demonstrated through extensive numerical results.

提出了一种基于超过空气计算的方法，探索了无线多接入通道的叠加属性，用于边缘机器学习中的快速全局模型聚合。