Conventional Federated Learning (FL) involves collaborative training of a global model while maintaining user data privacy. One of its branches, decentralized FL, is a serverless network that allows clients to own and optimize different local models separately, which results in saving management and communication resources. Despite the promising advancements in decentralized FL, it may reduce model generalizability due to lacking a global model. In this scenario, managing data and model heterogeneity among clients becomes a crucial problem, which poses a unique challenge that must be overcome: How can every client’s local model learn generalizable representation in a decentralized manner? To address this challenge, we propose a novel Decentralized FL technique by introducing Synthetic Anchors, dubbed as DeSA. Based on the theory of domain adaptation and Knowledge Distillation (KD), we theoretically and empirically show that synthesizing global anchors based on raw data distribution facilitates mutual knowledge transfer. We further design two effective regularization terms for local training: 1) REG loss that regularizes the distribution of the client’s latent embedding with the anchors and 2) KD loss that enables clients to learn from others. Through extensive experiments on diverse client data distributions, we showcase the effectiveness of DeSA in enhancing both inter- and intra-domain accuracy of each client. The implementation of DeSA can be found at: https://github.com/ubc-tea/DESA

Dataset auditing for machine learning (ML) models is a method to evaluate if a given dataset is used in training a model. In a Federated Learning setting where multiple institutions collaboratively train a model with their decentralized private datasets, dataset auditing can facilitate the enforcement of regulations, which provide rules for preserving privacy, but also allow users to revoke authorizations and remove their data from collaboratively trained models. This paper first proposes a set of requirements for a practical dataset auditing method, and then present a novel dataset auditing method called Ensembled Membership Auditing ( EMA ). Its key idea is to leverage previously proposed Membership Inference Attack methods and to aggregate data-wise membership scores using statistic testing to audit a dataset for a ML model. We have experimentally evaluated the proposed approach with benchmark datasets, as well as 4 X-ray datasets (CBIS-DDSM, COVIDx, Child-XRay, and CXR-NIH) and 3 dermatology datasets (DERM7pt, HAM10000, and PAD-UFES-20). Our results show that EMA meet the requirements substantially better than the previous state-of-the-art method. Our code is at:https://github.com/Hazelsuko07/EMA.

Our novel skin-feature visual-tracking algorithm enables anatomic vSLAM and (by extension) localization of clinical tools relative to the patient’s body. Tracking naturally occurring features is challenging due to patient uniqueness, deformability, and lack of an accurate a-priori 3D geometric model. Our method (i) tracks skin features in a smartphone-camera video sequence, (ii) performs anatomic Simultaneous Localization And Mapping (SLAM) of camera motion relative to the patient’s 3D skin surface, and (iii) utilizes existing visual methods to track clinical tool(s) relative to the patient’s reconstructed 3D skin surface. (We demonstrate tracking of a simulated ultrasound probe relative to the patient by using an Apriltag visual fiducial). Our skin-feature tracking method utilizes the Fourier-Mellin Transform for robust performance, which we incorporated and extend an existing Phase Only Correlation (POC) based algorithm to be suitable for our application of free-hand smartphone video, wherein the distance of the camera fluctuates relative to the patient. Our SLAM approach further utilizes Structure from Motion and Bundle Adjustment to achieve an accurate 3D model of the human body with minimal drift-error in camera trajectory. We believe this to be the first freehand smartphone-camera tracking of natural skin features for anatomic tracking of surgical tools, ultrasound probe, etc.