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Federated continual learning is a decentralized approach that enables edge devices to continuously learn new data, mitigating catastrophic forgetting while collaboratively training a global model. However, existing state-of-the-art approaches in federated continual learning focus primarily on learning continuously to classify discrete sets of images, leaving dense regression tasks such as depth estimation unaddressed. Furthermore, autonomous agents that use depth estimation to explore dynamic indoor environments inevitably encounter spatial and temporal shifts in data distributions. These shifts trigger a phenomenon called spatio-temporal catastrophic forgetting, a more complex and challenging form of catastrophic forgetting. In this paper, we address the fundamental research question: “Can we mitigate spatiotemporal catastrophic forgetting in federated continual learning for depth estimation in dynamic indoor environments?”. To address this question, we propose Local Online and Continual Adaptation (LOCA), the first approach to address spatio-temporal catastrophic forgetting in dynamic indoor environments. LOCA relies on two key algorithmic innovations: online batch skipping and continual local aggregation. Our extensive experiments show that LOCA mitigates spatio-temporal catastrophic forgetting and improves global model performance, while running on-device up to 3.35× faster and consuming 3.13× less energy compared to state-of-the-art. Thus, LOCA lays the groundwork for scalable autonomous systems that adapt in real time to learn private and dynamic indoor environments. 

Not AvailablDeploying monocular depth estimation on resource-constrained edge devices is a significant challenge, particularly when attempting to perform both training and inference concurrently. Current lightweight, self-supervised approaches typically rely on complex frameworks that are hard to implement and deploy in real-world settings. To address this gap, we introduce the first framework for Lightweight Training and Inference (LITI) that combines ready-to-deploy models with streamlined code and fully functional, parallel training and inference pipelines. Our experiments show various models being deployed for inference, training, or both inference and training, leveraging inputs from a real-time RGB camera sensor. Thus, our framework enables training and inference on resource-constrained edge devices for complex applications such as depth estimation. 

The effectiveness of digital contact tracing during extended outbreaks of airborne infectious diseases, such as COVID-19, influenza, or RSV, can be hindered by limited social compliance and delays in real-world testing. Prior work has shown the utility of graph learning for bidirectional contact tracing and multi-agent reinforcement learning (MARL) for disease mitigation; however, they rely on post-hoc analysis and full testing compliance, thus limiting real-time applicability. To address these limitations, we propose a new framework for online automated bidirectional contact tracing and disease-aware navigation. Our framework iteratively identifies infectious culprits, infers individual health statuses, and deploys agents to minimize infectious exposure without requiring Oracle health information. Our proposed framework achieves an average online backwards tracing F1-score of 92% and estimates the total case counts within 5% accuracy, even under conditions of probabilistic testing with significant social hesitancy. Additionally, our proposed agent-based navigation system can reduce the disease spread by 29%. These results demonstrate the framework’s potential to address critical gaps in traditional disease surveillance and mitigation models and improve real-time public health interventions. 

For rapidly spreading diseases where many cases show no symptoms, swift and effective contact tracing is essential. While exposure notification applications provide alerts on potential exposures, a fully automated system is needed to track the infectious transmission routes. To this end, our research leverages large-scale contact networks from real human mobility data to identify the path of transmission. More precisely, we introduce a new Infectious Path Centrality network metric that informs a graph learning edge classifier to identify important transmission events, achieving an F1-score of 94%. Additionally, we explore bidirectional contact tracing, which quarantines individuals both retroactively and proactively, and compare its effectiveness against traditional forward tracing, which only isolates individuals after testing positive. Our results indicate that when only 30% of symptomatic individuals are tested, bidirectional tracing can reduce infectious effective reproduction rate by 71%, thus significantly controlling the outbreak.