  The dramatic success of deep learning is largely due to the availability of
data. Data samples are often acquired on edge devices, such as smart phones,
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 datasets, 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. In this article,
we provide a unified systematic framework for federated learning in a manner
that encapsulates and highlights the main challenges that are natural to treat
using signal processing tools. We present a formulation for the federated
learning paradigm from a signal processing perspective, and survey a set of
candidate approaches for tackling its unique challenges. We further provide
guidelines for the design and adaptation of signal processing and communication
methods to facilitate federated learning at large scale.
