PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records

TL;DR

This paper introduces PMFL, a novel partial meta-federated learning algorithm that effectively handles heterogeneous data and tasks in medical applications, achieving faster training and better performance.

Contribution

The paper proposes a new partial meta-federated learning algorithm combining federated learning, meta-learning, and transfer learning for heterogeneous data.

Findings

Faster training speed on medical datasets

Superior performance on heterogeneous data

Effective handling of diverse medical data tasks

Abstract

Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources, especially when communication technology develops rapidly and an unprecedented amount of data could be collected on mobile devices nowadays. Federated learning method exploits not only the data but the computational power of all devices in the network to achieve more efficient model training. Nevertheless, while most traditional federated learning methods work well for homogeneous data and tasks, adapting the method to a different heterogeneous data and task distribution is challenging. This limitation has constrained the applications of federated learning in real-world contexts, especially in healthcare settings. Inspired by the fundamental idea of meta-learning, in this study we propose a new algorithm, which is an integration of federated learning and meta-learning, to tackle this issue. In addition, owing to the advantage of transfer learning for model generalization, we further improve our algorithm by introducing partial parameter sharing. We name this method partial meta-federated learning (PMFL). Finally, we apply the algorithms to two medical datasets. We show that our algorithm could obtain the fastest training speed and achieve the best performance when dealing with heterogeneous medical datasets.