Data Valuation Methods for Federated Learning

Abstract

Modern distributed networks such as smartphones, wearable devices, and self-driving vehicles generate a wealth of data. As the computation, storage, and battery capabilities of these devices grow, local data storage and processing become easier and more secure. This has led to a growing interest in federated learning which provides training of deep learning models while keeping the training data decentralized. During the training, the performance of deep learning models can be improved by filtering redundant, malicious, and abnormal samples with data valuation methods. In this work, the aim is to improve federated learning models by integrating data valuation methods into the training process. For this, a two-layered convolutional neural network is trained on the MNIST dataset inside a small federated learning network. The Shapley and Leave-one-out based data evaluation methods developed in this study have resulted in an 8.81% accuracy improvement during experiments conducted on the MNIST dataset with corrupted labels.