23 citations · 119 across the 32 of their papers we have counts for
7 papers · 1 filter
Faster Adaptive Federated Learning
Xidong Wu, Feihu Huang, Zhengmian Hu +1
Federated learning has attracted increasing attention with the emergence of distributed data. While extensive federated learning algorithms have been proposed for the non-convex di…
Fast Adaptive Federated Bilevel Optimization
Feihu Huang
Bilevel optimization is a popular hierarchical model in machine learning, and has been widely applied to many machine learning tasks such as meta learning, hyperparameter learning…
Adaptive Federated Minimax Optimization with Lower Complexities
Feihu Huang, Xinrui Wang, Junyi Li +1
Federated learning is a popular distributed and privacy-preserving learning paradigm in machine learning. Recently, some federated learning algorithms have been proposed to solve t…
Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization
Feihu Huang
Federated Learning is a popular distributed learning paradigm in machine learning. Meanwhile, composition optimization is an effective hierarchical learning model, which appears in…
Communication-Efficient Adam-Type Algorithms for Distributed Data Mining
Wenhan Xian, Feihu Huang, Heng Huang
Distributed data mining is an emerging research topic to effectively and efficiently address hard data mining tasks using big data, which are partitioned and computed on different…
Dateformer: Time-modeling Transformer for Longer-term Series Forecasting
Julong Young, Junhui Chen, Feihu Huang +1
Transformers have demonstrated impressive strength in long-term series forecasting. Existing prediction research mostly focused on mapping past short sub-series (lookback window) t…
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction
Junyi Li, Feihu Huang, Heng Huang
Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms and has been applied to many machine learning tasks such as data cleaning, few-s…