DNet: An efficient privacy-preserving distributed learning framework for healthcare systems

ABSTRACT

Medical data held in silos by institutions, makes it challenging to predict new trends and gain insights, as, sharing individual data leaks user privacy and is restricted by law. Meanwhile, the Federated Learning framework [11] would solve this problem by facilitating on-device training while preserving privacy. However, the presence of a central server has its inherent problems, including a single point of failure and trust. Moreover, data may be prone to inference attacks. This paper presents a Distributed Net algorithm called DNet to address these issues posing its own set of challenges in terms of high communication latency, performance, and efficiency. Four different networks have been discussed and compared for computation, latency, and precision. Empirical analysis has been performed over Chest X-ray Images and COVID-19 dataset. The theoretical analysis proves our claim that the algorithm has a lower communication latency and provides an upper bound. © Springer Nature Switzerland AG 2021.