  In this paper, we increase the availability and integration of devices in the
learning process to enhance the convergence of federated learning (FL) models.
To address the issue of having all the data in one location, federated
learning, which maintains the ability to learn over decentralized data sets,
combines privacy and technology. Until the model converges, the server combines
the updated weights obtained from each dataset over a number of rounds. The
majority of the literature suggested client selection techniques to accelerate
convergence and boost accuracy. However, none of the existing proposals have
focused on the flexibility to deploy and select clients as needed, wherever and
whenever that may be. Due to the extremely dynamic surroundings, some devices
are actually not available to serve as clients in FL, which affects the
availability of data for learning and the applicability of the existing
solution for client selection. In this paper, we address the aforementioned
limitations by introducing an On-Demand-FL, a client deployment approach for
FL, offering more volume and heterogeneity of data in the learning process. We
make use of the containerization technology such as Docker to build efficient
environments using IoT and mobile devices serving as volunteers. Furthermore,
Kubernetes is used for orchestration. The Genetic algorithm (GA) is used to
solve the multi-objective optimization problem due to its evolutionary
strategy. The performed experiments using the Mobile Data Challenge (MDC)
dataset and the Localfed framework illustrate the relevance of the proposed
approach and the efficiency of the on-the-fly deployment of clients whenever
and wherever needed with less discarded rounds and more available data.
