How Does Federated Learning Work in Cancer Research?
In federated learning, local institutions train a shared model using their own data. These local models periodically communicate with a central server, which aggregates the updates to form a global model. The global model is then redistributed back to the local institutions for further training. This iterative process continues until the model achieves satisfactory performance. By doing so, federated learning leverages the computational power and data of multiple entities while maintaining data sovereignty.
