5 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…