PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its
applications on real-world medical records
- URL: http://arxiv.org/abs/2112.05321v2
- Date: Sat, 9 Mar 2024 02:42:38 GMT
- Title: PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its
applications on real-world medical records
- Authors: Tianyi Zhang, Shirui Zhang, Ziwei Chen, Dianbo Liu
- Abstract summary: Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources.
We propose a new algorithm, which is an integration of federated learning and meta-learning, to tackle this issue.
We show that our algorithm could obtain the fastest training speed and achieve the best performance when dealing with heterogeneous medical datasets.
- Score: 11.252157002705484
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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