Learn fundamental security concepts and challenges in federated and collaborative learning environments through this comprehensive tutorial presented by Google researcher Adria Gascon. Explore the unique security vulnerabilities that arise when multiple parties collaborate on machine learning tasks without sharing raw data, including privacy attacks, model poisoning, and adversarial threats. Examine defense mechanisms and security protocols designed to protect federated learning systems, covering techniques such as secure aggregation, differential privacy, and robust aggregation methods. Understand the trade-offs between security, privacy, and model performance in distributed learning scenarios. Analyze real-world case studies and practical implementations of security measures in federated learning deployments. Gain insights into current research directions and emerging threats in the rapidly evolving landscape of collaborative machine learning security.