9 citations · 11 across the 6 of their papers we have counts for
6 papers
Low-Complexity Algorithm for Stackelberg Prediction Games with Global Optimality
Tong Wei, Yangjie Xu, Xinlin Wang +4
Stackelberg prediction games (SPGs) model strategic data manipulation in adversarial learning via a leader--follower interaction between a learner and a self-interested data provid…
FedRandom: Sampling Consistent and Accurate Contribution Values in Federated Learning
Arno Geimer, Beltran Fiz Pontiveros, Radu State
Federated Learning is a privacy-preserving decentralized approach for Machine Learning tasks. In industry deployments characterized by a limited number of entities possessing abund…
To Squelch or not to Squelch: Enabling Improved Message Dissemination on the XRP Ledger
Lucian Trestioreanu, Flaviene Scheidt, Wazen Shbair +3
With the large increase in the adoption of blockchain technologies, their underlying peer-to-peer networks must also scale with the demand. In this context, previous works highligh…
WallStreetFeds: Client-Specific Tokens as Investment Vehicles in Federated Learning
Arno Geimer, Beltran Fiz Pontiveros, Radu State
Federated Learning (FL) is a collaborative machine learning paradigm which allows participants to collectively train a model while training data remains private. This paradigm is e…
On the Volatility of Shapley-Based Contribution Metrics in Federated Learning
Arno Geimer, Beltran Fiz, Radu State
Federated learning (FL) is a collaborative and privacy-preserving Machine Learning paradigm, allowing the development of robust models without the need to centralize sensitive data…
Cross Domain Early Crop Mapping using CropSTGAN
Yiqun Wang, Hui Huang, Radu State
Driven by abundant satellite imagery, machine learning-based approaches have recently been promoted to generate high-resolution crop cultivation maps to support many agricultural a…