The Role of the Data Quality on Model Efficiency: An Exploratory Study on Centralised and Federated Learning

This paper investigates the impact that datasets of varying data quality levels have on centralized vs. federated learning models using experiments. We also investigate how the distribution of low-quality data across federated clients affects the models’ accuracy. Within the experiments we create datasets of increasingly worse data quality in terms of the following two data quality metrics; data accuracy and data completeness. This is done by perturbing (i.e., modifying) the datasets in order to decrease the quality of the datasets with regard to these two data quality metrics. Then, three experiments are conducted that investigates; i) the impact of decreased data accuracy on the models’ performance, ii) the impact of decreased data completeness, and iii) the effects of different distribution low-quality data on the clients used in the federated learning setup. The results reveal that the centralized model achieves 60.3% validation accuracy with low data accuracy and 58.7% with low data completeness. While the federated model performs better, achieving 69.3% validation accuracy with low data accuracy and 79.2% with low data completeness. The federated model is less affected by low data quality if the data quality is distributed evenly between its clients. Further, the federated learning setup displays certain attributes that make it more robust to data with low quality, compared to centralized learning. Uneven distribution of data quality between clients has a more negative impact on federated learning compared to even distribution.