Towards Privacy-Preserving and Explainable CNN-Based Image Classification

A Federated Learning and XAI Framework Grounded in Fashion-MNIST Engineering

Abstract

The paper introduces a two-axis extension of the standard Convolutional Neural Network (CNN) image classification studies; the technical exploration is based on an experimental baseline of Fashion-MNIST and generalizes its results to two frontier research directions of pressing societal importance Federated Learning (FL) to train distributed models privately and Explainable Artificial Intelligence (XAI) to make transparent and clinically interpretable decisions. It is a three-block sequential CNN (with 32, 64, and 64 filters Convolutional layers, max-pooling, dense classification and softmax output) that is trained on the 15,000 sample Fashion-MNIST test data with Adam optimizer categorical cross-entropy in 15 epochs (resulting in 89.57 percent accuracy and macro-averaged F1-score of 0.90). This performance profile per-class, i.e., high F1 on morphologically distinct classes (Trouser: 0.98; Bag: 0.98; Sandal: 0.96) and significantly lower performance on visually confusable classes (Shirt: 0.71) is systematically studied to incentivise a Federated Learning architecture that can quickly train CNNs on decentralised and non-IID data partitions without access to raw training examples and a Gradient-weighted Class Activation Mapping (Grad-CAM) XAI addition that can make Combined, these extensions map out a technically sound research path to deploy privacy-conscious, transparent CNN classifiers in high-stakes areas of application such as clinical diagnostic imaging, federated retail AI, and image pathology across institutions.