Research on Distributed Data Analytics in Edge Computing focuses on designing methods and frameworks to process and analyze data collaboratively across multiple distributed edge nodes, enabling low-latency, scalable, and efficient insights close to data sources. This area addresses challenges such as heterogeneous devices, dynamic workloads, limited computational and storage resources, network variability, and the need for real-time decision-making. Key research directions include distributed machine learning and deep learning for edge analytics, adaptive task scheduling and load balancing, and edge–cloud collaborative data processing frameworks. Other emerging topics involve privacy-preserving and secure distributed analytics, fault-tolerant and resilient data pipelines, and energy- and latency-aware computation strategies. Additionally, research on context-aware data aggregation, event-driven analytics, and multi-objective optimization for throughput, accuracy, and resource utilization represents significant avenues for advancing intelligent, efficient, and scalable distributed edge computing systems.