3 papers
cs.LG2026
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…
cs.LG2025
Collaborative Batch Size Optimization for Federated Learning
Arno Geimer, Karthick Panner Selvam, Beltran Fiz Pontiveros
Federated Learning (FL) is a decentralized collaborative Machine Learning framework for training models without collecting data in a centralized location. It has seen application a…
cs.CR2020
ÆGIS: Shielding Vulnerable Smart Contracts Against Attacks
Christof Ferreira Torres, Mathis Baden, Robert Norvill +3
In recent years, smart contracts have suffered major exploits, costing millions of dollars. Unlike traditional programs, smart contracts are deployed on a blockchain. As such, they…