A New Communication Efficient Federated Online Distillation for Non-IID Data: Application to Fetal Brain Ultrasound Plane Classification

Machine learning has achieved notable success in medical imaging; however, its reliability remains constrained by the quality and quantity of training data, particularly in specialized domains such as fetal brain ultrasound. Federated learning (FL) offers a collaborative alternative by enabling multiple institutions to train shared models without exposing sensitive data. Yet, its effectiveness often degrades under non-independent and identically distributed (non-IID) data and its communication demands can be prohibitive. To address these challenges, we propose FedFB, a communication-efficient federated learning framework that integrates online ensemble knowledge distillation for privacy-aware collaborative learning. In FedFB, multiple teacher models trained locally on private datasets collectively distill their knowledge into a lightweight dual-branch student network enhanced with an auxiliary attention–convolution module, without increasing model complexity. The distillation process leverages a small auxiliary public dataset for soft-label transfer, reducing data exposure by avoiding the exchange of raw private data or model parameters, while relying on shared supervisory signals for knowledge transfer. Experimental results on fetal brain ultrasound datasets demonstrate that FedFB achieves 91.07% accuracy, 91.28% precision, 91.03% recall, and 91.15% F1-score under the non-IID conditions, while reducing communication overhead by more than 85% compared to traditional FL methods. Furthermore, the framework’s robustness was validated on brain tumor MRI and chest X-ray datasets, confirming its generalization capability across distinct medical imaging modalities.