4 papers · 1 filter
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…
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…
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…
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…