9 citations · 29 across the 20 of their papers we have counts for
11 papers · 1 filter
HyperNAS: Enhancing Architecture Representation for NAS Predictor via Hypernetwork
Jindi Lv, Yuhao Zhou, Yuxin Tian +3
Time-intensive performance evaluations significantly impede progress in Neural Architecture Search (NAS). To address this, neural predictors leverage surrogate models trained on pr…
Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach
Yuhao Zhou, Jindi Lv, Yuxin Tian +3
Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative learning, yet data heterogeneity remains a critical challenge. While existing metho…
Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints
Yuhao Zhou, Yuxin Tian, Jindi Lv +5
In the realm of high-frequency data streams, achieving real-time learning within varying memory constraints is paramount. This paper presents Ferret, a comprehensive framework desi…
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing
Yuhao Zhou, Yuxin Tian, Mingjia Shi +4
The exponential growth in model sizes has significantly increased the communication burden in Federated Learning (FL). Existing methods to alleviate this burden by transmitting com…
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
Yuxin Tian, Mouxing Yang, Yuhao Zhou +5
Conventional federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the federated label-noise (F-LN) problem. Wor…
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Mingjia Shi, Yuhao Zhou, Kai Wang +4
Classical federated learning (FL) enables training machine learning models without sharing data for privacy preservation, but heterogeneous data characteristic degrades the perform…
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence
Yuhao Zhou, Mingjia Shi, Yuanxi Li +3
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning. While methods utilizing spars…
Personalized Federated Learning with Hidden Information on Personalized Prior
Mingjia Shi, Yuhao Zhou, Qing Ye +1
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes global servers and collaborative clients to achieve privacy-preserving global m…
Communication-Efficient Federated Learning with Compensated Overlap-FedAvg
Yuhao Zhou, Ye Qing, Jiancheng Lv
Petabytes of data are generated each day by emerging Internet of Things (IoT), but only few of them can be finally collected and used for Machine Learning (ML) purposes due to the…
HPSGD: Hierarchical Parallel SGD With Stale Gradients Featuring
Yuhao Zhou, Qing Ye, Hailun Zhang +1
While distributed training significantly speeds up the training process of the deep neural network (DNN), the utilization of the cluster is relatively low due to the time-consuming…
DBS: Dynamic Batch Size For Distributed Deep Neural Network Training
Qing Ye, Yuhao Zhou, Mingjia Shi +2
Synchronous strategies with data parallelism, such as the Synchronous StochasticGradient Descent (S-SGD) and the model averaging methods, are widely utilizedin distributed training…