  Many AI platforms, including traffic monitoring systems, use Federated
Learning (FL) for decentralized sensor data processing for learning-based
applications while preserving privacy and ensuring secured information
transfer. On the other hand, applying supervised learning to large data
samples, like high-resolution images requires intensive human labor to label
different parts of a data sample. Multiple Instance Learning (MIL) alleviates
this challenge by operating over labels assigned to the 'bag' of instances. In
this paper, we introduce Federated Multiple-Instance Learning (FedMIL). This
framework applies federated learning to boost the training performance in
video-based MIL tasks such as vehicle accident detection using distributed CCTV
networks. However, data sources in decentralized settings are not typically
Independently and Identically Distributed (IID), making client selection
imperative to collectively represent the entire dataset with minimal clients.
To address this challenge, we propose DPPQ, a framework based on the
Determinantal Point Process (DPP) with a quality-based kernel to select clients
with the most diverse datasets that achieve better performance compared to both
random selection and current DPP-based client selection methods even with less
data utilization in the majority of non-IID cases. This offers a significant
advantage for deployment on edge devices with limited computational resources,
providing a reliable solution for training AI models in massive smart sensor
networks.
