Accurate diagnosis of breast cancer in dense breasts requires expert radiologists to examine multiple ultrasound images per patient. This diagnosis procedure is tedious, time-consuming and prone to misdiagnosis due to human fatigue. AI-aided diagnosis systems can help alleviate this burden. However, vast amounts of data from multiple hospitals, diverse patient demographics, imaging scanners, and protocols are required to develop accurate, robust, and generalizable AI models. Obtaining such a mixture of data is quite challenging due to privacy concerns, data ownership issues, and regulatory constraints. To address this problem, we introduce a privacy-preserving and regulatory-compliant method for breast cancer diagnosis from ultrasound images using federated learning. This allows hospitals to keep their data on their premises while collaboratively training the AI model, only exchanging the model parameters trained on their private data. Experimental evaluation within a real federated data setting shows that our method achieved higher diagnostic accuracy than standardized centralized training while ensuring compliance with data privacy regulations.