Federated continual learning for privacy-preserving chest radiograph classification.

Multi-site deep learning for chest radiographs runs into two problems that are usually treated separately: privacy regulations prevent hospitals from pooling raw images, and clinical workflows change over time in ways that cause sequential model updates to overwrite earlier knowledge. Federated learning addresses the first problem, but most FL systems are built for static data distributions. Federated continual learning (FCL) handles both, though the methods that perform best on benchmarks tend to require public surrogate datasets or stored image replay - neither of which is easy to justify in a hospital context. We propose DP-FedEPC (Differentially Private Federated Elastic Prototype Consolidation), which combines elastic weight consolidation (EWC), prototype-based rehearsal, and client-side DP-SGD within a standard FedAvg workflow. EWC keeps weight updates from drifting away from parameters learned on earlier tasks. A small bank of latent prototypes - not raw images - holds class geometry stable across task shifts. Every client trains under DP-SGD, and we report the achieved [Formula: see text] for each noise level explicitly (Table 7). We train on CheXpert and validate externally on MIMIC-CXR. Beyond macro-AUROC, we report per-finding performance and forgetting scores (Table 3), prototype geometry and failure cases (Figs. 2-4), and a full cost breakdown against baselines (Table 8).