federated-brain-trajectory-gnn

You can predict infant brain connectivity evolution across multiple centers without sharing raw data by using federated multi-trajectory GNNs. This approach applies FedAvg-style parameter aggregation so hospitals collaborate on model training while keeping T1-w, T2-w, and DTI data localized.

The best way to handle sparse longitudinal neuroimaging data is using a two-step imputation pipeline combined with an auxiliary regularizer. These mechanisms ensure longitudinal consistency and improve trajectory forecasting performance even when multi-center infant brain datasets have limited samples.

Yes, you can use federated learning for multi-modal brain connectivity studies with T1-w, T2-w, and DTI modalities. The model employs graph-trajectory encoders, a shared condition generator, and per-trajectory decoders to process multi-modal infant brain data across different hospitals.

Federated parameter aggregation works by applying FedAvg-style updates to share only model parameters across centers, never raw neuroimaging data. This privacy-preserving collaboration enables multi-center infant brain development studies to forecast connectivity trajectories without exposing sensitive patient scans.

Yes, federated GNN training is specifically designed for infant brain development studies with small sample sizes. By aggregating parameters across centers and using auxiliary regularization, it effectively boosts model performance and longitudinal consistency despite limited local datasets.