This repository aggregates academic and practical resources on federated learning, a distributed machine learning approach that trains models across decentralized data sources while preserving privacy. It organizes papers by top-tier venues and subfields such as graph neural networks, tabular data, computer vision, and natural language processing, alongside links to FL frameworks, datasets, surveys, workshops, and educational tutorials.