Fuzzy Federated Learning for Privacy-Preserving Detection of Adolescent Idiopathic Scoliosis

As a distributed intelligent paradigm, fuzzy federated learning (FuzzyFL) can reduce the uncertainty and noise of biomedical data and is suited to enhance the accurate detection of adolescent idiopathic scoliosis (AIS). The advanced paradigm requires the hospitals to share the gradient of the fuzzy deep neural network (FDNN) rather than biomedical data. Not only that, the recent research works have been devoted to privacy-preserving FuzzyFL for secure AIS detection that adds differential privacy-based noise to the gradients against membership inference attack, attribute inference attack. However, a novel reconstruction attack called gradient leakage attack (GLA) on inferring biomedical data over the gradient brings the security challenges to FuzzyFL and, thus, has a negative influence on AIS detection. It is natural to ask a fundamental question: Can differentially private FuzzyFL for AIS detection over biomedical data defend GLA? In this article, we construct a privacy-preserving FuzzyFL framework called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PrivateFuzzyFL</monospace> that offers a great opportunity to present the systematic evaluation of the private FDNN threatened by the GLAs. In our experiments on a set of chest X-ray images and four FDNNs, we compare more than ten private fuzzy federated optimization algorithms in terms of the defense effect and the utility cost and derive that, first, the existing private FDNNs in FuzzyFL can offer a certain amount of privacy protection for biomedical data against the GLA; and second, the perturbation algorithm with better defense effect usually causes the worse AIS detection of the FDNN.