Distributed Estimation

Distributed estimation focuses on collaboratively estimating parameters from data collected across multiple, geographically dispersed nodes, aiming to improve accuracy and efficiency compared to centralized approaches. Current research emphasizes optimizing resource allocation (e.g., energy, communication bandwidth) through techniques like Fisher information maximization and value-of-information censoring, often employing Kalman filtering, ADMM, and multi-armed bandit algorithms. This field is crucial for applications like sensor networks and federated learning, enabling efficient and privacy-preserving data analysis in large-scale systems while addressing challenges like communication constraints and heterogeneous data sources.