ESTRO 2023 - Abstract Book

S239

Saturday 13 May

ESTRO 2023

The flowchart of the proposed PPFL is shown in Figure 1. We have devised a synthesis procedure to design a matrix encryption scheme for privacy and a modified FL algorithm, so that: 1) local models of the 'standard' FL are immersed/embedded in its training models; and 2) it works on encrypted global models. Matrix encryption is formulated at the server that maps the original models' parameters to higher-dimensional parameters and enforces that the modified FL system converges to an encrypted version of the original optimal solution. We recover the original optimal model by using the invertibility of the transformation. Results The proposed PPFL scheme is implemented for a classification task on chest X-ray images (CheXpert dataset) distributed across 5 sites. A DenseNet-121 is trained jointly across all sites. Locally, the models are trained with hyper-parameters: learning rate: 1e-4, local batch: 16, optimizer: Adam, and loss function: BCE. Each FL round consists of 3 epochs, and 3 FL rounds were executed. The comparison of the performance of the original FL and the proposed PPFL models is shown in Figure 2.