Distributed　modeling　and　monitoring　are　commonly　used　in　modern　industries.　Because　of　the　ever-growing　demands　for　energy　conservation　and　carbon　dioxide　emission　reduction,　attempts　have　been　made　to　improve　the　energy　efficiency　of　distributed　systems.　Here,　we　propose　a　hierarchical　democratized　learning　framework　to　optimize　distributed　computation　and　communication　consumptions.　First,　we　split　edge　devices　into　logical　learning　groups　which　cooperate　with　the　regional　task-related　server.　Then,　each　learning　group　performs　hierarchical　learning　through　the　generalization　and　specialization　procedures.　Through　hierarchical　clustering,　the　computation　tasks　cooperate　in　a　more　efficient　way　with　reduced　communication　requirements　at　the　same　time.　We　conducted　experiments　on　the　widely　used　datasets,　FMNIST　and　CIFAR-10,　demonstrating　lower　computation　and　communication　costs　of　hierarchical　democratic　modeling　compared　with　existing　federated　analytics　models.　©　2023　IEEE.