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researcher

Shuai Zhang

12 papers hereh-index 10593 citations14 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author7

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG4
  • cs.DC3
  • cs.AI1
same name
  • Shuai Zhang — 50 papers, h 26
  • Shuai Zhang — 27 papers, h 16
  • Shuai Zhang — 19 papers, h 15
  • Shuai Zhang — 17 papers, h 16
  • Shuai Zhang — 16 papers, h 19
  • Shuai Zhang — 15 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202023
most citedPerformance Optimization for Federated Person Re-identification via Benchmark Analysis

93 citations · 215 across the 12 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023

MAS: Towards Resource-Efficient Federated Multiple-Task Learning

Weiming Zhuang, Yonggang Wen, Lingjuan Lyu +1

Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous FL tas…

cs.LG2023

Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators

Sikai Bai, Shuaicheng Li, Weiming Zhuang +7

Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fracti…

cs.LG2022★ 2 cited

Smart Multi-tenant Federated Learning

Weiming Zhuang, Yonggang Wen, Shuai Zhang

Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous traini…

cs.LG2022★ 35 cited

Divergence-aware Federated Self-Supervised Learning

Weiming Zhuang, Yonggang Wen, Shuai Zhang

Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.