Federated Outlier Detection for Astronomical Data: Performance Analysis on Commercial Clouds - Lopes, Camila; Ferreira, Wesley; Gschwend, Julia; Costa, Luiz Nicolaci da; Silva, Rafael Ferreira da; Mattoso, Marta; Paes, Aline; Oliveira, Daniel de (Anais do Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD), 2025)
Astronomical surveys such as the Dark Energy Survey (DES) and the upcoming Legacy Survey of Space and Time (LSST) produce massive volumes of observational data, demanding scalable and efficient data analysis techniques. Among these, Machine Learning (ML) has become a key tool for extracting patterns and detecting anomalies in large astronomical catalogs. However, traditional centralized ML approaches are impractical in this scenario, due to data transfer bottlenecks, storage constraints, and privacy concerns. Federated Learning (FL) offers a decentralized alternative by training models across distributed data sources, reducing transfer costs and preserving data locality. However, configuring and deploying FL workflows is challenging due to client heterogeneity and data distribution. This paper explores the use of FL for outlier detection in large astronomical catalogs, using DES as a proxy for LSST. We emulate FL deployments on Amazon Web Services (AWS) cloud, evaluating various configurations of compute resources. Our results evidence the trade-offs between training time and financial cost, providing insights into the configuration of FL workflows for large-scale LSST data.