Data Governance and Privacy Risks in Distributed Deep Learning for Biomedical and Neurotechnology Applications: Medical Imaging and Brain-Computer Interfaces

Deep learning systems trained on unusually sensitive personal data diagnostic medical images and raw neural signals are increasingly deployed using distributed and collaborative training architectures intended to preserve privacy by keeping raw data local. This paper examines whether that architectural promise holds across two structurally different domains: distributed (federated) training for medical image segmentation, and brain-computer interface (BCI) systems that process continuous neural signal data. Drawing on the technical privacy-attack literature, medical AI governance literature, and the emerging neurorights and neurotechnology-regulation literature, this paper argues that federated learning's privacy guarantee is narrower than commonly assumed it removes centralized data pooling but does not eliminate membershipinference and model-inversion risk while BCI systems face a distinct and arguably harder governance problem: neural data is not adequately captured by existing biometric or health-data categories, and consentbased governance models are strained by data that can support probabilistic inference about mental states the user did not intend to disclose. The paper compares governance responses across both domains data protection regulation (GDPR, HIPAA), technical privacy defenses, and the emerging neurorights framework (UNESCO's 2025 Recommendation on the Ethics of Neurotechnology; U.S. state-level neural data laws), and proposes a set of governance principles applicable to both distributed medical imaging and BCI deployment. The paper does not present original empirical data or attack demonstrations; its contribution is a comparative synthesis grounded in independently verified sources.