Towards Automating the AI Operations Lifecycle
Matthew Arnold, Jeffrey Boston, et al.
MLSys 2020
Federated Learning is a novel approach to machine learning without the need to collect data in a central place. Issues of privacy concerns and regulatory restrictions make it impossible or expensive to bring all data to a centralized machine learning cluster. Federated learning helps to overcome these issues by collaboratively training a machine learning model without transmitting any raw data. In this talk, we present a novel implementation of a Histogram-Based Gradient Boosting Tree algorithm for Federated Learning and its advantages over various other Federated Learning approaches.
Matthew Arnold, Jeffrey Boston, et al.
MLSys 2020
Shiqiang Wang, Nathalie Baracaldo Angel, et al.
NeurIPS 2022
Ingkarat Rak-amnouykit, Ana Milanova, et al.
ICLR 2021
Amit Alfassy, Assaf Arbelle, et al.
NeurIPS 2022