Distributed data aggregation in edge computing has emerged as a key research area focused on efficiently collecting, processing, and summarizing data from heterogeneous and geographically distributed edge devices while minimizing latency, bandwidth usage, and energy consumption. Research papers in this domain explore methods for in-network aggregation, hierarchical processing, and edge–fog–cloud collaboration to handle massive volumes of real-time data generated by applications such as smart cities, industrial IoT, autonomous vehicles, and healthcare monitoring. Studies emphasize the use of machine learning, federated learning, and AI-driven techniques for adaptive aggregation, anomaly detection, and predictive analytics at the edge, reducing the need for extensive data transmission to centralized clouds. Recent works also investigate security- and privacy-preserving aggregation frameworks that leverage encryption, differential privacy, and blockchain to protect sensitive information during distributed processing. Additionally, load balancing, fault tolerance, and resilient aggregation strategies are explored to ensure continuous and reliable data processing under dynamic network conditions and device mobility. Overall, distributed data aggregation research in edge computing demonstrates its critical role in enabling scalable, real-time, and secure analytics while optimizing resource utilization across distributed infrastructures.