Differential privacy in federated learning has emerged as a promising solution for big data applications to achieve strong privacy guarantees. While prior work assumes that the privacy requirements are homogeneous across all clients, in practice, privacy requirements can differ across clients. In this paper, we introduce a cohort-based (ϵ, δ)-DP framework where privacy requirements and data distribution differ across these client cohorts. We show that the performance of existing differentially private stochastic algorithms degrade significantly for heterogeneous privacy scenarios, especially when the data is non-independent and identically distributed (non-iid). Moreover, we propose two novel continual learning-based DP training methods — DP-Synaptic intelligence (DP-SI) and DP-Rehearsal (DP-R) — to improve the model performance of cohorts with heterogeneous privacy budgets. We evaluate our approach on real datasets and show that our techniques outperform baseline techniques. Furthermore, our approach adapts to post-hoc privacy budget relaxations, providing greater flexibility in training models without significantly impacting performance.

Rooftop solar deployments are an excellent source for generating clean energy. As a result, their popularity among homeowners has grown significantly over the years. Unfortunately, estimating the solar potential of a roof requires homeowners to consult solar consultants, who manually evaluate the site. Recently there have been efforts to automatically estimate the solar potential for any roof within a city. However, current methods work only for places where LIDAR data is available, thereby limiting their reach to just a few places in the world. In this paper, we propose DeepRoof, a data-driven approach that uses widely available satellite images to assess the solar potential of a roof. Using satellite images, DeepRoof determines the roof's geometry and leverages publicly available real-estate and solar irradiance data to provide a pixel-level estimate of the solar potential for each planar roof segment. Such estimates can be used to identify ideal locations on the roof for installing solar panels. Further, we evaluate our approach on an annotated roof dataset, validate the results with solar experts and compare it to a LIDAR-based approach. Our results show that DeepRoof can accurately extract the roof geometry such as the planar roof segments and their orientation, achieving a true positive rate of 91.1% in identifying roofs and a low mean orientation error of 9.3 degree. We also show that DeepRoof's median estimate of the available solar installation area is within 11% of a LIDAR-based approach.

Buildings consume over 40% of the total energy in modern societies and improving their energy efficiency can significantly reduce our energy footprint. In this paper, we present WattHome, a data-driven approach to identify the least energy efficient buildings from a large population of buildings in a city or a region. Unlike previous approaches such as least squares that use point estimates, WattHome uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the parameter distribution of a building. Further, it compares them with similar homes in a given population using widely available datasets. WattHome also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in different settings. Moreover, we present results from a case study from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41%, 23.73%, and 0.51% homes have poor building envelope, heating, and cooling system faults respectively.