Federated edge learning (FEEL), a distributed machine learning paradigm, has become a viable option for developing learning models while maintaining data privacy. In this paper, we concentrate on a semi-decentralized FEEL (SD-FEEL) framework, taking into account the limited training data in a ...
semi-decentralized federated edge learning (SD-FEEL), a unique FEEL architecture, was recently put up by [20]. Instead of focusing on the functionality of data storage and task distribution [21], the edge servers in this system perform edge aggregation throughout each training round, allowing ...