This repository curates academic and practical resources about federated learning, including links to top ML/CV/AI conference proceedings, research papers organized by topic (model aggregation, personalization, security, optimization, fairness), and code repositories. It serves as a centralized reference for researchers and practitioners tracking advancements in privacy-preserving distributed machine learning.