Towards Interpretable Federated Learning

• URL: http://arxiv.org/abs/2302.13473v1
• Date: Mon, 27 Feb 2023 02:06:18 GMT
• Title: Towards Interpretable Federated Learning
• Authors: Anran Li, Rui Liu, Ming Hu, Luu Anh Tuan, Han Yu
• Abstract summary: Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. It is important to balance the need for performance, privacy-preservation and interpretability, especially in mission critical applications such as finance and healthcare. We conduct comprehensive analysis of the representative IFL approaches, the commonly adopted performance evaluation metrics, and promising directions towards building versatile IFL techniques.
• Score: 19.764172768506132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespread adoption, it is important to balance the need for performance, privacy-preservation and interpretability, especially in mission critical applications such as finance and healthcare. Thus, interpretable federated learning (IFL) has become an emerging topic of research attracting significant interest from the academia and the industry alike. Its interdisciplinary nature can be challenging for new researchers to pick up. In this paper, we bridge this gap by providing (to the best of our knowledge) the first survey on IFL. We propose a unique IFL taxonomy which covers relevant works enabling FL models to explain the prediction results, support model debugging, and provide insights into the contributions made by individual data owners or data samples, which in turn, is crucial for allocating rewards fairly to motivate active and reliable participation in FL. We conduct comprehensive analysis of the representative IFL approaches, the commonly adopted performance evaluation metrics, and promising directions towards building versatile IFL techniques.

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