FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image Classification

2025-01-06

AI Summary

To address the challenges of small-sample learning, data silos, and privacy sensitivity in lithological microscopic image classification for shale oil exploration, this paper proposes an end-to-end distributed framework integrating transfer learning and federated learning. The core contribution is a fine-grained tuning aggregation (FTA) strategy: clients asynchronously fine-tune pre-trained models (e.g., ResNet, VGG, EfficientNet) on local lithological data and upload only critical-layer parameters; a weighted adaptive aggregation mechanism is then employed to enhance global convergence and generalization under non-IID data conditions. Evaluated on a real-world lithological image dataset, the method achieves 98.2% accuracy and 97.6% F1-score—outperforming FedAvg by 4.3% and approaching the performance of centralized training—while substantially surpassing existing baselines in both accuracy and robustness.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Data Scarcity
• Privacy Preservation
• Rock Image Classification

Innovation

• Federal Transfer Learning
• Privacy Protection
• Rock Microscopy Image Recognition