Federated learning (FL) is a rapidly growing research area focused on enabling collaborative machine learning across multiple devices or edge nodes without the need to share raw data, making it particularly relevant for privacy-sensitive and distributed environments. Research papers in this domain explore algorithms, architectures, and frameworks for decentralized model training in applications such as healthcare, IoT, finance, and smart cities. Key contributions include communication-efficient FL protocols, personalization techniques, privacy-preserving mechanisms (e.g., differential privacy and secure aggregation), and integration with edge/fog computing for low-latency inference. Recent studies also address challenges such as heterogeneous data distributions, system scalability, fault tolerance, adversarial attacks, and incentive mechanisms for collaborative participation. By enabling decentralized, privacy-aware, and efficient learning, federated learning research continues to advance intelligent, secure, and adaptive systems across diverse real-world applications.