Federated learning for privacy preservation in edge computing has emerged as a key research area, enabling collaborative model training across distributed edge devices without sharing raw data, thereby safeguarding sensitive information while leveraging collective intelligence. Research papers in this domain explore techniques that combine federated learning with privacy-enhancing mechanisms such as differential privacy, secure multi-party computation, homomorphic encryption, and blockchain to ensure data confidentiality and integrity. Studies highlight applications in smart healthcare, autonomous vehicles, industrial IoT, and mobile edge computing, where user data is highly sensitive and low-latency, real-time analytics are required. Recent works investigate challenges such as communication efficiency, heterogeneous data distributions, model poisoning attacks, and adversarial manipulation in federated learning settings, proposing adaptive aggregation, robust optimization, and context-aware strategies. Moreover, hybrid frameworks integrating edge–fog–cloud architectures are explored to enhance scalability, reliability, and performance while maintaining strong privacy guarantees. Overall, research in federated learning for privacy preservation in edge computing demonstrates the potential to build intelligent, collaborative, and privacy-respecting edge ecosystems.