Our paper, “Tournament-Based Pretraining to Accelerate Federated Learning”, has been accepted for presentation at the 4th ACM International Workshops on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S). In this work, we introduce three innovative variants of a serverless federated learning framework, specifically addressing challenges associated with leveraging edge data. We introduce tournament-based pretraining that significantly enhances model performance. With these federated learning advancements, we aim to enable researchers to move beyond hurdles and focus on advancing scientific applications.