12 citations · 17 across the 9 of their papers we have counts for
3 papers · 1 filter
FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity
Ferdinand Kahenga, Antoine Bagula, Patrick Sello +1
Federated learning in practice must contend with heterogeneous feature spaces, severe non-IID data, and scarce labels across clients. We present FedFusion, a federated transfer-lea…
FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
Ferdinand Kahenga, Antoine Bagula, Sajal K. Das +1
Federated Learning (FL) has emerged as a powerful paradigm for privacy-preserving model training, yet deployments in sensitive domains such as healthcare face persistent challenges…
Tackling Selfish Clients in Federated Learning
Andrea Augello, Ashish Gupta, Giuseppe Lo Re +1
Federated Learning (FL) is a distributed machine learning paradigm facilitating participants to collaboratively train a model without revealing their local data. However, when FL i…