  In this paper we consider online distributed learning problems. Online
distributed learning refers to the process of training learning models on
distributed data sources. In our setting a set of agents need to cooperatively
train a learning model from streaming data. Differently from federated
learning, the proposed approach does not rely on a central server but only on
peer-to-peer communications among the agents. This approach is often used in
scenarios where data cannot be moved to a centralized location due to privacy,
security, or cost reasons. In order to overcome the absence of a central
server, we propose a distributed algorithm that relies on a quantized,
finite-time coordination protocol to aggregate the locally trained models.
Furthermore, our algorithm allows for the use of stochastic gradients during
local training. Stochastic gradients are computed using a randomly sampled
subset of the local training data, which makes the proposed algorithm more
efficient and scalable than traditional gradient descent. In our paper, we
analyze the performance of the proposed algorithm in terms of the mean distance
from the online solution. Finally, we present numerical results for a logistic
regression task.
