Samuel Horváth

Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity

2022 · arXiv (Cornell University)

Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., mobile phone devices, with an incentive to collaborate in solving a machine learning problem coordinated by a central server. FL was proposed …

Federated Learning with Regularized Client Participation

2023 · arXiv (Cornell University)

Federated Learning (FL) is a distributed machine learning approach where multiple clients work together to solve a machine learning task. One of the key challenges in FL is the issue of partial participation, which occurs …

Redefining Contributions: Shapley-Driven Federated Learning

2024 · arXiv (Cornell University)

Federated learning (FL) has emerged as a pivotal approach in machine learning, enabling multiple participants to collaboratively train a global model without sharing raw data. While FL finds applications in various domains such as healthcare …