MERGING MODELS ON AN EDGE SERVER

Systems and methods are provided for merging models for use in an edge server under the multi-access edge computing environment. In particular, a model merger selects a layer of a model based on a level of memory consumption in the edge server and determines sharable layers based on common properties of the selected layer. The model merger generates a merged model by generating a single instantiation of a layer that corresponds to the sharable layers. A model trainer trains the merged model based on training data for the respective models to attain a level of accuracy of data analytics above a predetermined threshold. The disclosed technology further refreshes the merged model upon observing a level of data drift that exceeds a predetermined threshold. The refreshing of the merged model includes detaching and/or splitting consolidated sharable layers of sub-models in the merged model. By merging models, the disclosed technology reduces memory footprints of models used in the edge server, rectifying memory scarcity issues in the edge server.