1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Emulating Heterogeneous Client Execution in Federated Learning
Arno Geimer
FL systems are inherently subject to client heterogeneity arising from differences in hardware capabilities. We propose a realistic evaluation framework for hardware-aware federate…
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…