4 papers
Decentralized EM Algorithm for Gaussian Mixtures under Data Heterogeneity and Partial Labeling
Xuetong Li, Shuyuan Wu, Bin Du +1
We systematically study several network-based Expectation-Maximization (EM) algorithms for the Gaussian mixture model within decentralized federated learning (DFL). Our theoretical…
Causal Inference for Network Autoregression Model: A Targeted Minimum Loss Estimation Approach
Yong Wu, Shuyuan Wu, Xinwei Sun +1
We study estimation of the average treatment effect (ATE) from a single network in observational settings with interference. The weak cross-unit dependence is modeled via an endoge…
Adaptive Decentralized Federated Learning for Robust Optimization
Shuyuan Wu, Feifei Wang, Yuan Gao +2
In decentralized federated learning (DFL), the presence of abnormal clients, often caused by noisy or poisoned data, can significantly disrupt the learning process and degrade the…
Federated Learning of Quantile Inference under Local Differential Privacy
Leheng Cai, Qirui Hu, Shuyuan Wu
In this paper, we investigate federated learning for quantile inference under local differential privacy (LDP). We propose an estimator based on local stochastic gradient descent (…