2 papers
cs.LG2025
Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning
Haoran Zhang, Zejun Gong, Zekai Li +3
Federated learning (FL) allows edge devices to collaboratively train models without sharing local data. As FL gains popularity, clients may need to train multiple unrelated FL mode…
cs.LG2023
3FM: Multi-modal Meta-learning for Federated Tasks
Minh Tran, Roochi Shah, Zejun Gong
We present a novel approach in the domain of federated learning (FL), particularly focusing on addressing the challenges posed by modality heterogeneity, variability in modality av…