  Sudden glare from trailing vehicles significantly increases driving safety
risks. Existing anti-glare technologies such as electronic, manually-adjusted,
and electrochromic rearview mirrors, are expensive and lack effective
adaptability in different lighting conditions. To address these issues, our
research introduces an intelligent rearview mirror system utilizing novel
all-liquid electrochromic technology. This system integrates IoT with ensemble
and federated learning within a cloud edge collaboration framework, dynamically
controlling voltage to effectively eliminate glare and maintain clear
visibility. Utilizing an ensemble learning model, it automatically adjusts
mirror transmittance based on light intensity, achieving a low RMSE of 0.109 on
the test set. Furthermore, the system leverages federated learning for
distributed data training across devices, which enhances privacy and updates
the cloud model continuously. Distinct from conventional methods, our
experiment utilizes the Schmidt-Clausen and Bindels de Boer 9-point scale with
TOPSIS for comprehensive evaluation of rearview mirror glare. Designed to be
convenient and costeffective, this system demonstrates how IoT and AI can
significantly enhance rearview mirror anti-glare performance.
