MFIFL: Federated Learning With Attention-Based Aggregation for Microscopic Fungal Images

An accurate classification of fungal morphology is essential for clinical diagnosis. Centralized deep learning approaches raise concerns about data privacy and struggle with heterogeneous data distributions. To address these challenges, this study proposes an attention-guided federated learning framework for multi-class fungal morphology classification using the DeFungi dataset. The proposed method integrates an improved FedAtt attention mechanism at the client level with an attention-aware federated aggregation strategy to improve global model consistency under non-IID data settings. Extensive experiments demonstrate that the proposed framework achieves stable convergence, balanced class-wise performance, and statistically reliable improvements. The proposed model achieved 93.73% test accuracy, and the FAA aggregation mechanism improved performance by 8.92% over FedAvg in a privacy-preserving federated learning setting. These results highlight the effectiveness of attention-aligned aggregation for robust and privacy-preserving medical image classification, making the proposed framework suitable for real-world clinical deployment.