PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records
This addresses the limitation of federated learning in real-world healthcare applications where data and tasks are heterogeneous, though it appears incremental as it builds on existing federated and meta-learning methods.
The authors tackled the challenge of applying federated learning to heterogeneous data and tasks, particularly in healthcare, by proposing a partial meta-federated learning (PMFL) algorithm that integrates federated and meta-learning with partial parameter sharing. They demonstrated that PMFL achieves the fastest training speed and best performance on two medical datasets.
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.