Secure fair aggregation based on category grou** in federated learning
J Zhou, J Hu, J Xue, S Zeng - Information Fusion, 2025 - Elsevier
Traditionally, privacy and fairness have been recognized as having different goals in
federated learning. Privacy requires data features to be as undetectable as possible …
federated learning. Privacy requires data features to be as undetectable as possible …
[HTML][HTML] Controlling update distance and enhancing fair trainable prototypes in federated learning under data and model heterogeneity
Heterogeneous federated learning (HtFL) has gained significant attention due to its ability to
accommodate diverse models and data from distributed combat units. The prototype-based …
accommodate diverse models and data from distributed combat units. The prototype-based …