FTA-FTL：一种用于岩性显微图像分类的微调聚合联邦迁移学习方案
FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image Classification
AI总结：
针对岩性显微图像数据稀缺与隐私保护问题，提出微调聚合联邦迁移学习方案FTA-FTL，并通过多指标实验证明其性能接近集中式方法。
摘要：
岩性识别是刻画油藏特征的一项关键活动，而处理岩性显微图像是研究化石和矿物以及对页岩油勘探进行地质评价的一项重要技术。在此方面，深度学习（DL）技术是构建鲁棒分类器模型的有力方法。然而，收集并构建大型数据集仍存在相当大的挑战。迁移学习和数据增强技术已成为解决该问题的流行方法。此外，由于多种原因，尤其是数据隐私，个人、组织和行业公司往往不愿共享其敏感数据和信息。联邦学习（FL）应运而生，用于在多个去中心化边缘服务器上训练高精度的中心模型，而无需传输敏感数据，从而保护敏感数据并增强安全性。本研究包含两个阶段；第一阶段是利用迁移学习在小型数据集上开展岩性显微图像分类。在此过程中，研究针对该分类任务全面比较了多种预训练DL模型架构。在第二阶段，我们将分类任务形式化为一种联邦迁移学习（FTL）方案，并提出了一种用于联邦学习的微调聚合策略（FTA-FTL）。为开展全面的实验研究，研究纳入了准确率、f1分数、精确率、特异度、灵敏度（召回率）和混淆矩阵等多项指标。结果具有很好的一致性，证实了所提方案的有效性，并表明所提出的FTA-FTL算法足以在岩性显微图像分类任务上取得与集中式实现近似相同的结果。
英文摘要：
Lithology discrimination is a crucial activity in characterizing oil reservoirs, and processing lithology microscopic images is an essential technique for investigating fossils and minerals and geological assessment of shale oil exploration. In this way, Deep Learning (DL) technique is a powerful approach for building robust classifier models. However, there is still a considerable challenge to collect and produce a large dataset. Transfer-learning and data augmentation techniques have emerged as popular approaches to tackle this problem. Furthermore, due to different reasons, especially data privacy, individuals, organizations, and industry companies often are not willing to share their sensitive data and information. Federated Learning (FL) has emerged to train a highly accurate central model across multiple decentralized edge servers without transferring sensitive data, preserving sensitive data, and enhancing security. This study involves two phases; the first phase is to conduct Lithology microscopic image classification on a small dataset using transfer learning. In doing so, various pre-trained DL model architectures are comprehensively compared for the classification task. In the second phase, we formulated the classification task to a Federated Transfer Learning (FTL) scheme and proposed a Fine-Tuned Aggregation strategy for Federated Learning (FTA-FTL). In order to perform a comprehensive experimental study, several metrics such as accuracy, f1 score, precision, specificity, sensitivity (recall), and confusion matrix are taken into account. The results are in excellent agreement and confirm the efficiency of the proposed scheme, and show that the proposed FTA-FTL algorithm is capable enough to achieve approximately the same results obtained by the centralized implementation for Lithology microscopic images classification task.
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