most citedIronForge: An Open, Secure, Fair, Decentralized Federated Learning

4 citations · 8 across the 4 of their papers we have counts for

collaborators

7 papers

q-fin.GN2023

Cryptocurrency in the Aftermath: Unveiling the Impact of the SVB Collapse

Qin Wang, Guangsheng Yu, Shiping Chen

In this paper, we explore the aftermath of the Silicon Valley Bank (SVB) collapse, with a particular focus on its impact on crypto markets. We conduct a multi-dimensional investiga…

cs.CR2023

BRC-20: Hope or Hype

Qin Wang, Guangsheng Yu

BRC-20 (short for Bitcoin Request for Comment 20) token mania was a key storyline in the middle of 2023. Setting it apart from conventional ERC-20 token standards on Ethereum, BRC-…

cs.LG20233 cited

A Secure Aggregation for Federated Learning on Long-Tailed Data

Yanna Jiang, Baihe Ma, Xu Wang +4

As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes. I…

cs.CR20231 cited

Distributed Trust Through the Lens of Software Architecture

Sin Kit Lo, Yue Liu, Guangsheng Yu +3

Distributed trust is a nebulous concept that has evolved from different perspectives in recent years. While one can attribute its current prominence to blockchain and cryptocurrenc…

cs.LG20232 cited

Blockchained Federated Learning for Internet of Things: A Comprehensive Survey

Yanna Jiang, Baihe Ma, Xu Wang +5

The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comp…

cs.LG20234 cited

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

Guangsheng Yu, Xu Wang, Caijun Sun +5

Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the ov…