Federated Model Heterogeneity

Unlike standard federated averaging which requires identical model architectures, heterogeneous frameworks decouple local model design from global knowledge sharing. Knowledge distillation and logit transfer allow a ResNet-50 at Hospital A and a Vision Transformer at Hospital B to collaboratively learn without ever aligning their internal parameters. The global consensus emerges from shared predictions on a public proxy dataset rather than parameter averaging.

Model heterogeneity permits each institution to design architectures that match their available compute resources:

• GPU-rich research hospitals can deploy large transformer-based models
• Community clinics with edge hardware can train lightweight MobileNet or EfficientNet variants
• Legacy infrastructure sites can use classical statistical models All contribute to the same federated objective without a lowest-common-denominator constraint.

Clinical data silos often contain fundamentally different feature spaces. A heterogeneous framework allows:

• Radiology departments to train 3D convolutional networks on volumetric CT scans
• Pathology labs to train graph neural networks on whole-slide images
• EHR systems to train tabular models on structured clinical records Each modality-specific architecture extracts the most relevant representations from its native data format before contributing to the federated consensus.

The primary mechanism enabling model heterogeneity is federated distillation. Instead of exchanging model weights, clients share soft labels or logit outputs on a public, unlabeled reference dataset. The central server aggregates these predictions to train a global student model. This approach:

• Eliminates the requirement for identical model architectures
• Reduces communication payloads compared to gradient transfer
• Provides an additional layer of privacy by obfuscating internal model structure

Model heterogeneity naturally supports personalized federated learning objectives. Each institution maintains a locally specialized architecture that captures site-specific patient demographics, imaging protocols, or disease prevalence patterns. The federated distillation process ensures these local models still benefit from the collective knowledge of the network, avoiding the performance degradation of fully isolated training while preserving local clinical relevance.

Heterogeneous architectures can dramatically reduce network overhead in bandwidth-constrained healthcare environments:

• Logit vectors are typically orders of magnitude smaller than full model parameter sets
• Asynchronous participation becomes feasible since there is no global model state to synchronize
• Selective knowledge sharing allows clients to only transmit predictions on clinically relevant proxy samples This makes federated learning viable across rural telehealth networks and low-resource settings.

A consortium of five hospitals trains a collaborative diagnostic model where each site uses a different convolutional neural network (CNN) backbone tailored to its GPU capacity. A well-funded academic center deploys a ResNet-152, while a rural clinic uses a lightweight MobileNetV3. Knowledge is transferred via federated distillation, where each site shares soft-label predictions on a public chest X-ray dataset rather than model weights. The aggregated ensemble achieves 97.2% sensitivity for pneumonia detection, matching the performance of a centrally trained monolithic model without ever moving a single pixel of patient data.

A network of implantable loop recorders and consumer smartwatches collaboratively trains an arrhythmia classifier. High-fidelity medical implants run a bi-directional LSTM on raw ECG waveforms, while wrist-worn devices execute a temporal convolutional network (TCN) on photoplethysmography (PPG) signals. The central server aggregates knowledge using heterogeneous federated averaging with a shared representation layer. This architecture allows resource-constrained wearables to benefit from the diagnostic precision of clinical-grade implants without ever accessing protected health information.

Three genomics laboratories with different sequencing platforms—Illumina NovaSeq, Oxford Nanopore, and PacBio—collaborate on a federated variant caller. Each lab trains a specialized deep learning model optimized for its sequencer's error profile: a CNN for Illumina's short reads, an RNN for Nanopore's long noisy reads, and a transformer for PacBio's circular consensus sequences. Federated aggregation occurs via model-agnostic meta-learning (MAML), producing a global initialization that rapidly adapts to any sequencing technology. The system identifies pathogenic variants with 99.1% concordance against a centrally trained gold standard.

A federated network of oncology departments trains patient-specific drug response predictors where local model architectures reflect institutional treatment patterns. A comprehensive cancer center uses a graph neural network (GNN) incorporating protein-protein interaction networks, while a community hospital employs a gradient-boosted tree ensemble on structured EHR features. The global model is synthesized via federated ensemble distillation, where a central student model learns from the aggregated predictions of all heterogeneous teachers. The system achieves AUC 0.91 for predicting chemotherapy response across 12 cancer types.

Three intensive care units with heterogeneous monitoring equipment collaboratively train an early-warning system for patient deterioration. A neuro-ICU with continuous EEG uses a spatiotemporal graph CNN, a cardiac ICU with high-frequency waveforms deploys a WaveNet architecture, and a general ICU with sparse vital signs uses a transformer with missingness embeddings. The heterogeneous models share knowledge through a federated attention transfer mechanism, where each local model learns to attend to clinically relevant temporal patterns distilled from peer institutions. The ensemble reduces false alarms by 42% while maintaining 98% sensitivity for septic shock prediction.

A nationwide network of pathology labs trains a cancer grading system where scanner heterogeneity necessitates diverse model architectures. Labs with high-throughput whole-slide scanners train vision transformers (ViTs) on gigapixel images, while smaller clinics with smartphone-based microscopes use EfficientNet-B0 on tiled image patches. Federated aggregation employs heterogeneous knowledge distillation with a shared prototype layer that aligns feature representations across architectures. The system classifies breast cancer histology grades with Cohen's kappa of 0.89 against expert pathologist consensus, demonstrating that architectural diversity does not compromise diagnostic agreement.