Wearable computer×Federated learning

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
  1/4
• Guowen XuBeijing Institute of Big Data Research
  1/3
• Sen LiuUniversity of Guelph
  1/3
• Jindong WangZhejiang University
  1/2
• Yiqiang ChenMicrosoft (United States)
  1/2
• Xin QinZhejiang University
  1/2
• Geyong MinUniversity of Passau
  1/2
• Haomiao YangBeijing Institute of Big Data Research
  1/2
• Meng HaoBeijing Institute of Big Data Research
  1/2
• Bert ArnrichBoğaziçi University
  1/1

• Nanyang Technological UniversitySG
  68/50
• Tsinghua UniversityCN
  76/42
• Chinese Academy of SciencesCN
  127/22
• Zhejiang UniversityCN
  57/41
• Beihang UniversityCN
  52/30
• Carnegie Mellon UniversityUS
  58/25

• Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence2019 · 576 citations · DOI ↗
  2
• Integrating blockchain for data sharing and collaboration in mobile healthcare applications2017 · 700 citations · DOI ↗
  3
• Federated Learning in a Medical Context: A Systematic Literature Review2021 · 283 citations · DOI ↗
  3
• Privacy-preserving and communication-efficient federated learning in Internet of Things2021 · 92 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.