69 citations · 91 across the 8 of their papers we have counts for
8 papers
Knowledge-Injected Federated Learning
Zhenan Fan, Zirui Zhou, Jian Pei +4
Federated learning is an emerging technique for training models from decentralized data sets. In many applications, data owners participating in the federated learning system hold…
A dual approach for federated learning
Zhenan Fan, Huang Fang, Michael P. Friedlander
We study the federated optimization problem from a dual perspective and propose a new algorithm termed federated dual coordinate descent (FedDCD), which is based on a type of coord…
Fair and efficient contribution valuation for vertical federated learning
Zhenan Fan, Huang Fang, Xinglu Wang +4
Federated learning is an emerging technology for training machine learning models across decentralized data sources without sharing data. Vertical federated learning, also known as…
Improving Fairness for Data Valuation in Horizontal Federated Learning
Zhenan Fan, Huang Fang, Zirui Zhou +4
Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federat…
Cardinality-constrained structured data-fitting problems
Zhenan Fan, Huang Fang, Michael P. Friedlander
A memory-efficient framework is described for the cardinality-constrained structured data-fitting problem. Dual-based atom-identification rules are proposed that reveal the structu…
Polar Deconvolution of Mixed Signals
Zhenan Fan, Halyun Jeong, Babhru Joshi +1
The signal demixing problem seeks to separate a superposition of multiple signals into its constituent components. This paper studies a two-stage approach that first decompresses a…
Polar Alignment and Atomic Decomposition
Zhenan Fan, Halyun Jeong, Yifan Sun +1
Structured optimization uses a prescribed set of atoms to assemble a solution that fits a model to data. Polarity, which extends the familiar notion of orthogonality from linear se…
Bundle methods for dual atomic pursuit
Zhenan Fan, Yifan Sun, Michael P. Friedlander
The aim of structured optimization is to assemble a solution, using a given set of (possibly uncountably infinite) atoms, to fit a model to data. A two-stage algorithm based on gau…