|Shiqiang Wang||IBM T. J. Watson Research Center, USA|
|Tiffany Tuor||Imperial College London, United Kingdom (Great Britain)|
|Theodoros Salonidis||IBM Research, USA|
|Kin K Leung||Imperial College, United Kingdom (Great Britain)|
|Christian Makaya||IBM T. J. Watson Research Center, USA|
|Ting He||Penn State University, USA|
|Kevin S Chan||US Army Research Laboratory, USA|
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.