Communication-Efficient Federated Learning of Latent Patient Representations from Multi-Institutional EHRs

Electronic health records (EHRs) contain rich health information for uncovering latent patient representations and clinically meaningful subgroups. Learning from EHR data across institutions is valuable because representations learned at a single site may reflect local data patterns and lack generalizability, yet pooling patient-level records across institutions is often impractical because of privacy constraints and communication costs. We propose a communication-efficient, non-iterative federated spectral method for learning shared latent representations from multi-institutional EHRs, in which each institution shares aggregate marginal frequencies and a low-rank spectral summary while keeping patient-level records local. A central server aggregates these summaries with the pooled marginal frequencies to recover both the shared low-rank structure and the canonical row-normalization direction required for interpretable representation recovery. We prove row-wise recovery guarantees and error bounds, with a leading federated variance term matching a corresponding lower bound under explicit regularity conditions. Simulations show that our method closely matches pooled analysis in estimating latent representations, improves as more sites contribute summaries, and outperforms single-site and existing federated benchmarks across a range of data settings. Applied to real-world multi-facility EHR data for stroke patients, the method learns interpretable patient representations that highlight clinically distinct subgroups and could help inform subgroup-specific care.