METHODS AND SYSTEMS FOR IMPROVED FEDERATED LEARNING AND IMPLEMENTATIONS THEREOF

This disclosure provides novel methods and systems for novel two-way knowledge distillation-based federated learning framework (referred to as Fed2KD) that can work homogeneous models, e.g., models with different configurations, user preferences, and/or different properties of user devices. In the disclosed Fed2KD, the knowledge exchange between the global and local models is achieved by distilling the information into or out from a tiny model with unified configuration using a proxy dataset generated by conditional variational autoencoder (CVAE). In another aspect, an improved federated learning framework is disclosed that implements a federated learning-based Next Generation Radio Access Networks (NG-RAN) algorithm (referred to as FedNG). The disclosed FedNG can be implemented to address the limited capacity of the fronthaul links as well as privacy concerns.