CRII: RI: Federated Meta-Learning for Cross-Network Crime Analytics in Interdependent Environments

About This Grant

In today's interconnected digital landscape, where technology plays an increasingly crucial role in our lives, the realms of public safety and law enforcement have undergone a profound transformation. This digital convergence has opened new avenues for advanced analytics, providing valuable insights into various aspects of society, including a holistic understanding of crime dynamics. However, traditional crime analytics methods fall short in keeping up with the complexities of modern criminal activities. These methods are limited by their isolation and reliance on similar types of data and resources, making it difficult to adapt to our fast-moving, interconnected society. In response, our project presents a novel and comprehensive approach that transcends these barriers. By ensuring the protection of data and optimizing the efficiency of crime analytics processes, this project captures valuable information from diverse data sources, thereby facilitating a more robust understanding of crime patterns. The developed technologies from this research can be applied to many other fields such as healthcare, finance, and environmental monitoring. The project will integrate research with education and outreach to different groups including unrepresentative and minority students. The focus of this project is to enhance AI-driven crime analytics through the development of a federated meta-learning framework. This framework will facilitate efficient, private, and secure model training using multi-modal data. The initial research thrust focuses on creating a novel data labeling and rebalancing algorithm to promote fair model training across interconnected nodes, thereby advancing fairness in AI systems, while the integration of meta-learning during local model training tailors a proactive approach to alleviating computational burdens on individual devices, promoting practical efficiency. Building upon these foundations, the second research thrust emphasizes the handling of multi-modal data, showcasing the project's capability to navigate complexities within interdependent networks and effectively integrate diverse data sources. Finally, the third research thrust tailors distributed differential privacy mechanisms and introduces an adversarial agent classification technique, underscoring the proposal's dedication towards robust and secure model training. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.