Research on Federated Learning for Edge Computing focuses on enabling collaborative, decentralized model training across distributed edge devices while preserving data privacy and reducing communication overhead with central servers. This area addresses challenges such as heterogeneous hardware, limited computational resources, intermittent connectivity, and data heterogeneity across edge nodes. Key research directions include designing communication-efficient federated learning algorithms, adaptive aggregation techniques, and personalized models for non-IID (non-independent and identically distributed) data. Other emerging topics involve privacy-preserving mechanisms using differential privacy and secure multi-party computation, energy- and latency-aware federated learning, and edge–cloud collaborative training frameworks. Additionally, research on fault-tolerant and robust federated learning against adversarial attacks, incentive mechanisms for participant engagement, and integration with IoT and real-time applications represents significant avenues for advancing intelligent, secure, and efficient edge computing systems.