FEDEBA+: TOWARDS FAIR AND EFFECTIVE FEDERATED LEARNING VIA ENTROPY-BASED MODEL

Ensuring fairness is a crucial aspect of Federated Learning (FL), which enables the model to perform consistently across all clients. However, designing an FL algorithm that simultaneously improves global model performance and promotes fairness remains a formidable challenge, as achieving the latter often necessitates a trade-off with the former.