4 citations · 12 across the 6 of their papers we have counts for
6 papers
XFL: A High Performace, Lightweighted Federated Learning Framework
Hong Wang, Yuanzhi Zhou, Chi Zhang +4
This paper introduces XFL, an industrial-grade federated learning project. XFL supports training AI models collaboratively on multiple devices, while utilizes homomorphic encryptio…
SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction
Zhiqi Lin, Youshan Miao, Guodong Liu +10
With the growing model size, deep neural networks (DNN) are increasingly trained over massive GPU accelerators, which demands a proper parallelization plan that transforms a DNN mo…
Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data
Kai-Fung Chu, Lintao Zhang
Many application scenarios call for training a machine learning model among multiple participants. Federated learning (FL) was proposed to enable joint training of a deep learning…
OpEvo: An Evolutionary Method for Tensor Operator Optimization
Xiaotian Gao, Cui Wei, Lintao Zhang +1
Training and inference efficiency of deep neural networks highly rely on the performance of tensor operators on hardware platforms. Manually optimizing tensor operators has limitat…
RPC Considered Harmful: Fast Distributed Deep Learning on RDMA
Jilong Xue, Youshan Miao, Cheng Chen +3
Deep learning emerges as an important new resource-intensive workload and has been successfully applied in computer vision, speech, natural language processing, and so on. Distribu…
Episodic Memory Deep Q-Networks
Zichuan Lin, Tianqi Zhao, Guangwen Yang +1
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are kn…