解决联邦学习中模型置信度不可靠的问题，提升决策可信度。
Unlocking the Potential of Model Calibration in Federated Learning
- 根据客户端与全局模型的差异动态调整校准目标
- 在保持准确率的同时显著提升预测置信度可靠性
- 适用于多种联邦学习算法，适合实际应用决策场景
近年来，联邦学习（FL）方法不断演进以提升模型准确率，但其在实际决策场景中的应用仍受限于模型对预测结果的置信度可靠性。现有研究普遍忽视了这一关键问题。为此，本文提出非均匀校准联邦学习（NUCFL），一种将联邦学习与模型校准结合的通用框架。由于联邦学习环境存在固有的数据异构性，校准难度大，需确保在多样数据分布和客户端条件下都具备可靠性。NUCFL通过分析每个客户端局部模型与全局模型之间的统计关系，动态调整本地训练中的校准损失惩罚项，从而有效适应异构场景下的校准需求。实验表明，NUCFL在多种联邦学习算法上均表现出良好的灵活性与有效性，在不牺牲准确率的前提下显著提升模型校准性能。
原文摘要 · Abstract (English)
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 the global model in heterogeneous FL settings while not sacrificing accuracy. Extensive experiments show that NUCFL offers flexibility and effectiveness across various FL algorithms, enhancing accuracy as well as model calibration.
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