Integrating a Neural Network Library in Embedded Federated Learning.

Federated learning has become a popular collaborative machine learning approach in which clients use their local data for training. This results in bandwidth savings since the training data does not have to be transferred to a central node, and also protects the privacy of these data. However, doing federated learning in tiny Internet of Things (IoT) devices is challenging due to the resource constraints of the devices and lack of experimental tools. In this paper, we develop a federated learning implementation that allows training of different machine learning models on embedded IoT devices. Our starting point is a library of neural network models which we have developed. We analyze the metadata to describe these models, design the interaction process between clients and server of a federated learning network, and implement the software. We validate the system by conducting experiments with our implementation deployed on real embedded boards. We observe in a non-IID experiment the capacity of federated learning to provide a trained model whose capacities go beyond those that the node could train by itself. Being heterogeneous data distribution a realistic scenario in distributed IoT devices, we believe that our implementation can be useful for the community as a tool to explore the training of neural networks on microcontrollers with the support of federated learning.