In today’s data-driven world, AI models rely on vast amounts of information to improve accuracy and performance. However, gathering and storing sensitive data centrally raises privacy concerns and legal challenges. This is where Federated Learning (FL) steps in—a breakthrough AI training method that allows models to learn from distributed data sources without sharing raw data . By decentralizing the training process, Federated Learning enables AI systems to improve while keeping personal and sensitive data secure and private . Industries like healthcare, finance, cybersecurity, and smart devices are already leveraging this approach to balance AI advancements with data protection. In this article, we’ll explore: What Federated Learning is and how it works The key benefits compared to traditional AI training Real-world applications and industries adopting FL Challenges and limitations to consider How the Machine Learning Course in Kolka...