75 citations · 105 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning
Yae Jee Cho, Samarth Gupta, Gauri Joshi +1
Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round. While most prior works…
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
Yae Jee Cho, Jianyu Wang, Gauri Joshi
Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Several…