75 citations · 124 across the 15 of their papers we have counts for
15 papers
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
Yae Jee Cho, Luyang Liu, Zheng Xu +2
Foundation models (FMs) adapt well to specific domains or tasks with fine-tuning, and federated learning (FL) enables the potential for privacy-preserving fine-tuning of the FMs wi…
Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels
Yae Jee Cho, Gauri Joshi, Dimitrios Dimitriadis
Many existing FL methods assume clients with fully-labeled data, while in realistic settings, clients have limited labels due to the expensive and laborious process of labeling. Li…
On the Convergence of Federated Averaging with Cyclic Client Participation
Yae Jee Cho, Pranay Sharma, Gauri Joshi +3
Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL). Previous convergence analyses of FedAvg either assume full cl…
Maximizing Global Model Appeal in Federated Learning
Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li +2
Federated learning typically considers collaboratively training a global model using local data at edge clients. Clients may have their own individual requirements, such as having…
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi +2
Federated learning (FL) enables edge-devices to collaboratively learn a model without disclosing their private data to a central aggregating server. Most existing FL algorithms req…
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer
Yae Jee Cho, Jianyu Wang, Tarun Chiruvolu +1
Personalized federated learning (FL) aims to train model(s) that can perform well for individual clients that are highly data and system heterogeneous. Most work in personalized FL…