Non-IID Medical Imaging Data on COVID-19 in the Federated Learning Framework: Impact and Directions

After first appearing in December 2019, coronavirus disease 2019 (COVID-19) spread rapidly, leading to global effects and significant risks to health systems. The virus's high replication competence in the human lung accelerated the severity of lung pneumonia cases, resulting in a catastrophic death rate. Variable observations in the clinical testing of virus-related and patient-related cases across different populations led to ambiguous results. Medical and epidemiological studies on the virus effectively use imaging and scanning devices to help explain the virus's behavior and its impact on the lungs. Varying equipment resources and a lack of uniformity in medical imaging acquisition led to disorganized and widely dispersed data collection worldwide, while high heterogeneity in datasets caused a poor understanding of the virus and related strains, consequently leading to unstable results that could not be generalized. Hospitals and medical institutions, therefore, urgently need to collaborate to share and extract useful knowledge from these COVID-19 datasets while preserving the privacy of medical records. Researchers are turning to an emerging technology that enhances the reliability and accessibility of information without sharing actual patient data. Federated learning (FL) is a technique that learns distributed data locally, sharing only the weights of each local model to compute a global model, and has the potential to improve the generalization of diagnosis and treatment decisions. This study investigates the applicability of FL for COVID-19 under the impact of data heterogeneity, defining the lung imaging characteristics and identifying the practical constraints of FL in medical fields. It describes the challenges of implementation from a technical perspective, with reference to valuable research directions, and highlights the research challenges that present opportunities for further efforts to overcome the pitfalls of distributed learning performance. The primary objective of this literature review is to provide valuable insights that will aid in the formulation of effective technical strategies to mitigate the impact of data heterogeneity on the generalization of FL results, particularly in light of the ongoing and evolving COVID-19 pandemic.