A Federated Learning Framework for Brain Tumour Detection Using Xception Convolutional Neural Network with CKKS-Based Secure Aggregation

Abstract

Federated learning (FL) enables collaborative medical image analysis without centralising sensitive data, making it highly suitable for privacy-critical applications such as brain tumour detection from magnetic resonance imaging (MRI). However, conventional FL frameworks remain vulnerable to parameter-level information leakage through shared model updates, which may expose sensitive information about the underlying medical data. This study proposes a privacy-preserving FL framework that integrates Cheon-Kim-Kim-Song (CKKS)-based homomorphic encryption (HE) for secure aggregation with an Xception convolutional neural network (CNN) for multi-class brain tumour classification. In the proposed framework, local models are trained in plaintext on distributed clients, while the resulting model parameters are encrypted before transmission and aggregation. This approach ensures that the central server performs aggregation without gaining access to plaintext model updates, thereby improving confidentiality and reducing the risk of privacy breaches. Experiments conducted on a four-class brain MRI dataset demonstrate that the proposed approach achieves a global classification accuracy of 97.87%, with less than 1% performance degradation compared to conventional non-encrypted FL. Furthermore, a comprehensive analysis of encryption, decryption, and federated communication round latency is performed to evaluate the computational overhead introduced by CKKS-based HE. The experimental findings confirm the practical feasibility of integrating HE-assisted secure aggregation with deep learning-based FL systems for privacy-preserving medical imaging applications, while maintaining high diagnostic performance and reliable collaborative model training.