Emerging technologies and applications including
Internet of Things (IoT), social networking, and crowd-sourcing
generate large amounts of data at the network edge. Machine
learning models are often built from the collected data, to enable
the detection, classification, and prediction of future events.
Due to bandwidth, storage, and privacy concerns, it is often
impractical to send all the data to a centralized location. In this
paper, we consider the problem of learning model parameters
from data distributed across multiple edge nodes, without sending
raw data to a centralized place. Our focus is on a generic class
of machine learning models that are trained using gradient-
descent based approaches. We analyze the convergence rate of
distributed gradient descent from a theoretical point of view,
based on which we propose a control algorithm that determines
the best trade-off between local update and global parameter
aggregation to minimize the loss function under a given resource
budget. The performance of the proposed algorithm is evaluated
via extensive experiments with real datasets, both on a networked
prototype system and in a larger-scale simulated environment.
The experimentation results show that our proposed approach
performs near to the optimum with various machine learning
models and different data distributions.