Federated learning×Wearable technology

The Frontier Brief for this collision is still being written. The evidence below already reflects the current model run.

Pre-company space. No named players have staked this collision yet — an emerging pairing where the field, not a firm, is the story so far.

21 researchers publish on both sides of this collision without the fields themselves having met. Every name below is counted from papers in the corpus — not inferred.

• Hongwei LiXidian University
  4/1
• Guowen XuBeijing Institute of Big Data Research
  3/1
• Sen LiuUniversity of Guelph
  3/1
• Xin QinZhejiang University
  2/1
• Meng HaoBeijing Institute of Big Data Research
  2/1
• Jindong WangZhejiang University
  2/1
• Haomiao YangBeijing Institute of Big Data Research
  2/1
• Yiqiang ChenMicrosoft (United States)
  2/1
• Geyong MinUniversity of Passau
  2/1
• Wei WangThe University of Texas at Dallas
  1/1

• Nanyang Technological UniversitySG
  50/65
• Tsinghua UniversityCN
  42/68
• Chinese Academy of SciencesCN
  22/122
• Zhejiang UniversityCN
  41/54
• Beihang UniversityCN
  30/49
• Carnegie Mellon UniversityUS
  25/55

• Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence2019 · 576 citations · DOI ↗
  2
• Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities2017 · 1,131 citations · DOI ↗
  3
• Federated Learning in a Medical Context: A Systematic Literature Review2021 · 283 citations · DOI ↗
  3
• Toward Accurate Anomaly Detection in Industrial Internet of Things Using Hierarchical Federated Learning2021 · 257 citations · DOI ↗
  3

Counted from the corpus. Institution counts use best-effort affiliation (every author on a paper is paired with every institution on it), so read them as presence, not headcount.