4 papers
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation
Yun-Wei Chu, Dong-Jun Han, Christopher G. Brinton
Federated learning (FL) is a promising distributed machine learning paradigm that enables multiple clients to collaboratively train a global model. In this paper, we focus on a pra…
Unlocking the Potential of Model Calibration in Federated Learning
Yun-Wei Chu, Dong-Jun Han, Seyyedali Hosseinalipour +1
Over the past several years, various federated learning (FL) methodologies have been developed to improve model accuracy, a primary performance metric in machine learning. However,…
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks
Yun-Wei Chu, Dong-Jun Han, Seyyedali Hosseinalipour +1
A few recent studies have demonstrated that leveraging centrally pre-trained models can offer advantageous initializations for federated learning (FL). However, existing pre-traini…
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics
Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio +4
Conventional methods for student modeling, which involve predicting grades based on measured activities, struggle to provide accurate results for minority/underrepresented student…