37 citations · 90 across the 6 of their papers we have counts for
7 papers
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning
Charlie Hou, Hongyuan Zhan, Akshat Shrivastava +4
In Federated Learning (FL), accessing private client data incurs communication and privacy costs. As a result, FL deployments commonly prefinetune pretrained foundation models on a…
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Shengyuan Hu, Jack Goetz, Kshitiz Malik +3
Model compression is important in federated learning (FL) with large models to reduce communication cost. Prior works have been focusing on sparsification based compression that co…
Papaya: Practical, Private, and Scalable Federated Learning
Dzmitry Huba, John Nguyen, Kshitiz Malik +11
Cross-device Federated Learning (FL) is a distributed learning paradigm with several challenges that differentiate it from traditional distributed learning, variability in the syst…
Convex Latent Effect Logit Model via Sparse and Low-rank Decomposition
Hongyuan Zhan, Kamesh Madduri, Venkataraman Shankar
In this paper, we propose a convex formulation for learning logistic regression model (logit) with latent heterogeneous effect on sub-population. In transportation, logistic regres…
Federated Learning with Buffered Asynchronous Aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan +4
Scalability and privacy are two critical concerns for cross-device federated learning (FL) systems. In this work, we identify that synchronous FL - synchronized aggregation of clie…
Evaluating Lottery Tickets Under Distributional Shifts
Shrey Desai, Hongyuan Zhan, Ahmed Aly
The Lottery Ticket Hypothesis suggests large, over-parameterized neural networks consist of small, sparse subnetworks that can be trained in isolation to reach a similar (or better…
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction
Hongyuan Zhan, Gabriel Gomes, Xiaoye S. Li +2
Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting. Machine learning algorithms adjust mod…