Are You Catching Cosmic Rays or Just Checking Messages? Towards an Extremely Distributed Mobile Phone Cosmic Ray Observatory Carlyn Lee and Harvey Newman Division of Physics, Mathematics and Astronomy, California Institute of Technology, CA, USA Contact: Carlyn Lee Email: cblee@caltech.edu https://github.com/carlynlee/credo-api-tools/tree/playground/ References 1. Martın Abadi, et al., TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org. 2. Piotr Homola, et al., Cosmic-ray extremely dis- tributed observatory. Symmetry, 2020. 3. Jennifer Ngadiuba, et al., Compressing deep neural networks on fpgas to binary and ternary precision with hls4ml. Machine Learning: Science and Technology, 2(1):015001, Dec 2020. 4. Daniel Whiteson, et al., Searching for ultra-high energy cosmic rays with smartphones, Astroparticle Physics, Volume 79, 2016, Pages 1-9, ISSN 0927-6505. This project aims to design an architecture for applying Federated Learning (FL) to the analysis of cosmic ray event data collected through the Cosmic Ray Extremely Distributed Observatory (CREDO) project. CREDO gathers data from a global network of smartphones, enabling a large-scale citizen science initiative to study cosmic rays. The traditional approach of centralizing this data raises concerns about privacy and data ownership. We explore as a potential solution, allowing decentralized training of machine learning models directly on participants’ devices. We explore how FL can accommodate CREDO's requirements, such as handling heterogeneous data and robust model aggregation. We present our initial steps in designing an FL architecture tailored to the needs of the CREDO project, outline our architectural proposal, and discussing the potential benefits and the technical challenges. This sets the stage for future implementation and evaluation, aiming to enhance accountability and collaboration in scientific data analysis. Abstract Cluster Identification: Identified and visualized ten distinct clusters, each representing different events triggering the CREDO app. Device Comparison: Analysis reveals variance in detection patterns across devices, possibly indicating differences in device sensitivity or environmental conditions. Data Retrieval: Elasticsearch for query and image retrieval. Data stored as base64 encoded strings, corresponding to different devices and time frames. Image Processing: Decoded images from base64 format and resized to uniform dimensions for consistent analysis. Performed clustering based on extracted features using a pre-trained model, adapted to identify distinct patterns within the data. Cluster Visualization: Python scripts to compute and display the mean image for each cluster across multiple devices, enhancing interpretability of clustering results. Tools: Python (NumPy, Matplotlib, Pillow), ResNet50 deep learning model. q Visual analysis leveraged clustering and image processing techniques to dissect cosmic ray detection data. This approach highlighted variations across different devices and conditions, revealing possible patterns through visual data exploration. Such insights can be critical for refining detection strategies and enhancing the overall understanding of cosmic ray distributions. Future Work q Enhance Model Accuracy: Improve machine learning models and clustering precision. q Dataset Expansion: Enlarge dataset scope and integrate real-time data analysis for dynamic insights. q Federated Learning Implementation: q Local Model Training: Enable devices to train locally, sharing only model updates to minimize bandwidth use and enhance privacy. q Scalability: Use federated learning to keep data on local devices, addressing privacy concerns and managing large data volumes. q Collaborative Advancements: Combine insights across diverse devices, enhancing detection pattern accuracy and variety. q Managing asynchronous updates to models Discussion and Conclusions Objective: To analy