Federated Learning (FL) enables decentralized model training while preserving patient privacy, making it essential for medical AI applications. However, regulatory frameworks such as GDPR, CCPA, and LGPD mandate “the right to be forgotten”, requiring patient data removal from trained models upon request. This has driven growing interest in Federated Unlearning (FU), but existing methods require the collaborative participation of all clients, which is often impractical and raises privacy concerns. This paper proposes Maverick, a novel Collaboration-free FU framework that enables localized unlearning at the target client by minimizing model sensitivity, without requiring global collaboration from all clients to unlearn a target client. Theoretical analysis and extensive experiments on three medical imaging datasets, Colorectal Cancer Histology, Pigmented Skin Lesions, and Blood Cells, demonstrate Maverick’s effectiveness in sample, class, and client unlearning scenarios. Maverick ensures trustworthy FL in healthcare while complying with regulations. The code is publicly available at https://github.com/OngWinKent/Maverick .