Federated Enformer

Adapts the Enformer's transformer backbone for cross-silo federated learning by splitting the self-attention mechanism across institutional clients. Each participating site computes attention over its local genomic sequences, and only the gradient updates from the multi-head attention layers are transmitted to the central aggregation server. This preserves the model's ability to capture long-range cis-regulatory interactions up to 100 kb without exposing the underlying DNA sequences or epigenetic tracks.

Implements a secure multi-party computation (SMPC) protocol to aggregate model weight updates from the Enformer's prediction heads—spanning 5,313 human epigenetic tracks and gene expression outputs—without any single institution's gradient vector being inspectable. The central server receives only the cryptographically summed update, ensuring that an adversary cannot perform a model inversion attack to reconstruct a client's private ChIP-seq or CAGE data from the shared gradients.

Addresses the inherent non-IID challenge in federated genomics where different institutions may sequence distinct populations or focus on specific tissue types. The Federated Enformer employs FedProx optimization with a proximal term that penalizes local model divergence from the global consensus, stabilizing convergence when one hospital's data is exclusively cardiac tissue RNA-seq and another's is liver histone modification data. This prevents catastrophic forgetting of rare epigenomic patterns.

Integrates a local differential privacy (LDP) module that clips and noisifies the Enformer's weight updates on each client before transmission. A privacy accountant tracks the cumulative epsilon budget across training rounds, providing a formal mathematical guarantee that the presence of any single individual's genome in a local dataset is indistinguishable. This allows consortia to comply with GDPR and HIPAA data minimization mandates while jointly training a state-of-the-art gene expression predictor.

Applies structured sparsification and 8-bit quantization to the Enformer's massive gradient tensors before network transmission. Given that the Enformer contains ~250 million parameters, naive gradient exchange would require gigabytes of bandwidth per round. The compression module exploits the fact that many genomic feature channels exhibit sparse activation patterns, reducing communication overhead by over 90% without degrading the model's ability to predict transcription factor binding sites.

Extends the global Federated Enformer with personalized fine-tuning layers that adapt the shared epigenomic representations to each institution's specific biological context. After global aggregation, a client can fine-tune a lightweight adapter module on its private data to specialize in predicting population-specific expression quantitative trait loci (eQTLs) or rare variant effects without ever sharing those sensitive associations. This balances collaborative learning with institutional autonomy.

The specific federated topology suited for Federated Enformer, involving a small number of reliable institutional partners like hospitals or biobanks. Unlike cross-device settings with millions of unreliable phones, cross-silo assumes substantial local compute and stable connectivity, allowing for more complex architectures like Enformer to be trained collaboratively across a few high-value nodes.

A cryptographic protocol that ensures the central server can only compute the sum of model updates from all participating institutions, never inspecting an individual hospital's gradient. This prevents an honest-but-curious server from reconstructing private genomic features via gradient leakage, adding a critical layer of defense against model inversion attacks in the Federated Enformer pipeline.

A mathematical guarantee integrated into Federated Enformer to mask the contribution of any single genome. By injecting calibrated statistical noise into the aggregated model updates, it becomes provably difficult to determine if a specific individual's DNA sequence was included in a local training batch. This provides a quantifiable privacy budget (ε) for regulatory compliance.

A core challenge in Federated Enformer where local genomic datasets are not independently and identically distributed. A hospital specializing in rare diseases will have vastly different allele frequencies than a general population biobank. This statistical heterogeneity can cause local models to drift apart, requiring advanced optimization strategies beyond standard FedAvg to ensure global convergence.

A critical bottleneck for Federated Enformer due to the massive size of the transformer architecture. Transmitting full-precision weight updates for millions of parameters over standard hospital networks is prohibitive. Techniques like gradient compression, sparsification, and quantization are essential to reduce bandwidth usage and latency, making multi-institutional training practically feasible.