Interpretable Data Fusion for Distributed Learning: A Representative
Approach via Gradient Matching
Interpretable Data Fusion for Distributed Learning: A Representative
Approach via Gradient Matching
This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation. Unlike traditional distributed learning methods such as Federated Learning, which do not offer human interpretability, our method makes complex machine learning processes accessible and comprehensible. It achieves this by condensing extensive …