Jie Wen

International Journal of Machine Learning and Cybernetics · with Zhang · 2022

A researcher in the Science corpus: 1 paper; trusts 0 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Whom Jie Wen trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. H. Brendan McMahan
  2. Takayuki Nishio
  3. Robin C. Geyer
  4. Zhihua Cui
  5. Keith Bonawitz
  6. Ryo Yonetani
  7. Eider Moore
  8. Tassilo Johannes Klein
  9. Daniel Ramage

Papers from Jie Wen's perspective

Ordered by how strongly the researchers Jie Wen trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Communication-Efficient Learning of Deep Networks from Decentralized Data
  2. Practical Secure Aggregation for Privacy-Preserving Machine Learning
  3. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
  4. ImageNet classification with deep convolutional neural networks
  5. Federated Learning of Deep Networks using Model Averaging
  6. Protocols for secure computations
  7. Differentially Private Federated Learning: A Client Level Perspective
  8. Gradient-based learning applied to document recognition
  9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  10. Sharemind: A Framework for Fast Privacy-Preserving Computations

Their papers