Federated Learning (FL) allows the Internet of bioMedical Things (IoMT) devices to collaboratively train a global model without centralizing data, yet the heterogeneity of biomedical images poses challenges in achieving optimal performance for each IoMT device. Although many personalized FL methods have been proposed, their adaptability is often limited to data with certain levels of heterogeneity. In this paper, we propose a novel FL method to handle heterogeneous images in IoMT. First, we propose a gradient-fusion method that integrates both shared and local gradients into the locally adapted model for improved personalization. The shared gradients, aggregated from all IoMT devices through a learnable matrix, capture collective intelligence, while the local gradients, originating from each device’s data, reflect individual data distributions. This dynamic integration of collective and device-specific insights effectively mitigates data heterogeneity. Second, we propose a privacy-enhanced approach that delegates part of the gradient computation to devices, thereby protecting data privacy without compromising the efficacy of the gradient-fusion process. Finally, for enhanced performance, we introduce a layer-wise aggregation method to precisely measure the contribution of different layers in local models. Extensive evaluations on imaging datasets, featuring various types and degrees of data heterogeneity, demonstrate the superior performance of our methods over existing baselines.

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services. Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices. However, when users utilize these services, they inevitably expose personal information such as their ID and sensitive location to the servers. Due to untrustworthy servers and malicious attackers with colossal background knowledge, users’ personal information is at risk on these servers. Unfortunately, many privacy-preserving solutions for protecting trajectories have significantly decreased utility after deployment. We have come up with a new trajectory privacy protection solution that contraposes the area of interest for users. Firstly, Staying Points Detection Method based on Temporal-Spatial Restrictions (SPDM-TSR) is an interest area mining method based on temporal-spatial restrictions, which can clearly distinguish between staying and moving points. Additionally, our privacy protection mechanism focuses on the user’s areas of interest rather than the entire trajectory. Furthermore, our proposed mechanism does not rely on third-party service providers and the attackers’ background knowledge settings. We test our models on real datasets, and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.