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Unlocking the Potential of Model Calibration in Federated Learning by Chu, Yun-Wei; Han, Dong-Jun; Hosseinalipour, Seyyedali; Brinton, Christopher is a scholarly article available to read on EtoBox.

What is Unlocking the Potential of Model Calibration in Federated Learning about?

Over the past several years, various federated learning (FL) methodologies have been developed to improve model accuracy, a primary performance metric in machine learning. However, to utilize FL in practical decision-making scenarios, beyond considering accuracy, the trained model must also have a reliable confidence in each of its predictions, an aspect that has been largely overlooked in existing FL research. Motivated by this gap, we propose Non-Uniform Calibration for Federated Learning (NUCFL), a generic framework that integrates FL with the concept of model calibration. The inherent data heterogeneity in FL environments makes model calibration particularly difficult, as it must ensure reliability across diverse data distributions and client conditions. Our NUCFL addresses this challenge by dynamically adjusting the model calibration objectives based on statistical relationships between each client's local model and the global model in FL. In particular, NUCFL assesses the similarity between local and global model relationships, and controls the penalty term for the calibration loss during client-side local training. By doing so, NUCFL effectively aligns calibration needs for

Author

Chu, Yun-Wei; Han, Dong-Jun; Hosseinalipour, Seyyedali; Brinton, Christopher

Published

2024

Language

EN