An efficient hybrid federated learning framework for pneumonia diagnosis with proximal optimization and parameter-efficient adaptation

Pneumonia remains a serious worldwide health concern, particularly in low-resource countries, where prompt diagnosis is challenging. Early detection relies on chest radiography; however, data privacy rules and patient data fragmentation make it difficult to build AI models. Federated Learning allows for collaborative model training without sharing patient data, a promising solution. Standard federated learning methods, such as FedAvg, suffer from data heterogeneity and significant communication overhead. To overcome these constraints, this research proposes an upgraded federated framework with FedProx, which mitigates client drift in non-IID contexts by proximal optimization and Low-Rank Adaptation, a parameter-efficient fine-tuning technique that minimizes communication costs. Vision Transformers are utilized as the backbone architecture for chest X-ray categorization because they capture the global visual context more effectively than convolutional models. The proposed technique was validated for a pneumonia classification job utilizing the publicly available Chest X-Ray Images dataset, which was distributed across simulated clients to replicate real-world healthcare organizations. The model’s performance is measured using accuracy, precision, recall, F1-score, AUC, and system-level measures, including communication cost per round and convergence rate. Under conditions of non-IID heterogeneity of data, the proposed FedProx+LoRA framework demonstrated a classification accuracy of 88.5 which was higher compared to the centralized baseline (63.9%) or the standard FedAvg (60.9%), and showed a significant increase in comparison to FedProx itself (78.6%). Furthermore, the framework saved an overhead in communication by 97.4% in comparison to entire fine-tuning.