  Deployment of Deep Neural Networks in medical imaging is hindered by
distribution shift between training data and data processed after deployment,
causing performance degradation. Post-Deployment Adaptation (PDA) addresses
this by tailoring a pre-trained, deployed model to the target data distribution
using limited labelled or entirely unlabelled target data, while assuming no
access to source training data as they cannot be deployed with the model due to
privacy concerns and their large size. This makes reliable adaptation
challenging due to limited learning signal. This paper challenges this
assumption and introduces FedPDA, a novel adaptation framework that brings the
utility of learning from remote data from Federated Learning into PDA. FedPDA
enables a deployed model to obtain information from source data via remote
gradient exchange, while aiming to optimize the model specifically for the
target domain. Tailored for FedPDA, we introduce a novel optimization method
StarAlign (Source-Target Remote Gradient Alignment) that aligns gradients
between source-target domain pairs by maximizing their inner product, to
facilitate learning a target-specific model. We demonstrate the method's
effectiveness using multi-center databases for the tasks of cancer metastases
detection and skin lesion classification, where our method compares favourably
to previous work. Code is available at: https://github.com/FelixWag/StarAlign
