Federated learning trains a global model using data distributed across local nodes, and differs from centralized machine learning by moving the computation to the data in order to address the challenges of data ownership, privacy, computational power, and data storage. Previous federated learning research has addressed the effect of non independent and identically distributed data on federated learning [6]. Meanwhile, local models may have better performance if the test set is also non-IID [7]. However, there may be insufficient data on a node to train a local model for every node; hence the purpose of federated learning.
This research is the first, to our knowledge, to consider model performance on both a global test set and non-IID test set. Our experiments provide a original finding in that federated learning is only robust to non-IID data with constraints on the width and depth of a neural network. There is a tradeoff, however, between model complexity and feasibility of training the model on edge devices. Thus, we propose selective federated learning algorithm which greatly allows simpler models that fit on edge devices to be robust to highly non-IID data. For non-IID test sets, we prove that a converged federated model may converge to weights which do not provide the optimal local loss for an arbitrary chosen number of training samples on each node. Additionally, this thesis discusses the experiments that were conducted to examine the effects of model complexity, percentage of unbalanced data, and the current modes of model aggregation on model accuracy. For the experiments, we deployed federated learning library for multiple devices, Jetson Nano, Raspberry Pi, Macbook Pro, and Linux server and provide hardware benchmarks.Electrical and Computer Engineerin
