  Machine Learning (ML), and Deep Learning (DL) in particular, play a vital
role in providing smart services to the industry. These techniques however
suffer from privacy and security concerns since data is collected from clients
and then stored and processed at a central location. Federated Learning (FL),
an architecture in which model parameters are exchanged instead of client data,
has been proposed as a solution to these concerns. Nevertheless, FL trains a
global model by communicating with clients over communication rounds, which
introduces more traffic on the network and increases the convergence time to
the target accuracy. In this work, we solve the problem of optimizing accuracy
in stateful FL with a budgeted number of candidate clients by selecting the
best candidate clients in terms of test accuracy to participate in the training
process. Next, we propose an online stateful FL heuristic to find the best
candidate clients. Additionally, we propose an IoT client alarm application
that utilizes the proposed heuristic in training a stateful FL global model
based on IoT device type classification to alert clients about unauthorized IoT
devices in their environment. To test the efficiency of the proposed online
heuristic, we conduct several experiments using a real dataset and compare the
results against state-of-the-art algorithms. Our results indicate that the
proposed heuristic outperforms the online random algorithm with up to 27% gain
in accuracy. Additionally, the performance of the proposed online heuristic is
comparable to the performance of the best offline algorithm.

Machine Learning;Distributed, Parallel, and Cluster Computing