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During disasters, ensuring that critical response resources are efficiently allocated to the most appropriate locations is crucial for minimizing adverse impacts and saving lives. To this end, we present RADAR, a data-driven platform that integrates multisource GIS feeds (e.g., USGS earthquake alerts, Cal Fire wildfire perimeters) with facility and transportation data to support proactive planning and real-time recommendations that can be used by Emergency Operations Centers to guide populations to safety. RADAR uses policy-driven stable matching to optimize routing and resource assignment for evacuation planning and resource delivery. The aggregate model allocates populations in impacted facilities to alternate short-term facilities (e.g., hospitals), and a fine-grained extension for long-term senior-care facilities personalizes allocation using resident preferences, medical profiles, and social constraints. RADAR adapts as conditions evolve by utilizing historical data, live traffic, and changing facility status. We validated RADAR's efficacy in several disaster settings, including real events such as the Palisades wildfire and tabletop drills (earthquake and water-contamination scenarios) involving first responders. 

Not Federated Learning (FL) enables collaborative model training across decentralized clients without sharing raw data, yet faces significant challenges in real-world settings where client data distributions evolve dynamically over time. This paper tackles the critical problem of covariate and label shifts in streaming FL environments, where non-stationary data distributions degrade model performance and necessitate a middleware layer that adapts FL to distributional shifts. We introduce ShiftEx, a shift-aware mixture of experts framework that dynamically creates and trains specialized global models in response to detected distribution shifts using Maximum Mean Discrepancy for covariate shifts. The framework employs a latent memory mechanism for expert reuse and implements facility location-based optimization to jointly minimize covariate mismatch, expert creation costs, and label imbalance. Through theoretical analysis and comprehensive experiments on benchmark datasets, we demonstrate 5.5-12.9 percentage point accuracy improvements and 22-95 % faster adaptation compared to state-of-the-art FL baselines across diverse shift scenarios. The proposed approach offers a scalable, privacy-preserving middleware solution for FL systems operating in non-stationary, real-world conditions while minimizing communication and computational overhead. 

Data regulations like GDPR require systems to support data erasure but leave the definition of erasure open to interpretation. This ambiguity makes compliance challenging, especially in databases where data dependencies can lead to erased data being inferred from remaining data. We formally define a precise notion of data erasure that ensures any inference about deleted data, through dependencies, remains bounded to what could have been inferred before its insertion. We design erasure mechanisms that enforce this guarantee at minimal cost. Additionally, we explore strategies to balance cost and throughput, batch multiple erasures, and proactively compute data retention times when possible. We demonstrate the practicality and scalability of our algorithms using both real and synthetic datasets.