Federated Super-Resolution

Federated super-resolution allows hospitals to collaboratively train models that upscale low-resolution medical scans to diagnostic-quality high-resolution images without ever transferring patient data across institutional boundaries. Each site trains locally on its own imaging data, and only encrypted model updates—gradients or weights—are shared with a central aggregation server. This architecture ensures compliance with HIPAA, GDPR, and other healthcare privacy regulations while enabling access to diverse, multi-institutional training data that improves model robustness across scanner vendors and acquisition protocols.

Medical images acquired from different MRI or CT scanners exhibit significant variability in resolution, contrast, and noise characteristics. Federated super-resolution addresses this domain shift by training across heterogeneous hardware without centralizing data. Each institution's local model learns scanner-specific enhancement patterns, while the global aggregation process distills a generalized super-resolution function. This results in a model that can robustly enhance images from previously unseen scanner types, reducing the need for per-site calibration and enabling consistent diagnostic quality across multi-center clinical trials.

The core aggregation mechanism, typically Federated Averaging (FedAvg), coordinates the collaborative training process:

• Each client hospital trains a super-resolution model on its local low-resolution/high-resolution image pairs
• Model weights are transmitted to a central server, not images
• The server computes a weighted average of all client models
• Updated global model is redistributed for the next round This iterative process continues until convergence, with advanced variants like FedProx handling heterogeneous compute resources and SCAFFOLD correcting for client drift caused by non-IID data distributions across sites.

Clinical imaging datasets across hospitals are inherently non-IID (non-Independently and Identically Distributed) due to differences in patient demographics, disease prevalence, and imaging protocols. Federated super-resolution frameworks incorporate specialized techniques to address this:

• Personalized layers that remain local to each institution, capturing site-specific enhancement characteristics
• FedProx regularization that stabilizes training when clients have varying amounts of data
• Federated domain adaptation that aligns feature representations across sites without sharing data These methods ensure the global model does not overfit to dominant institutions while maintaining high performance on rare pathologies.

Super-resolution models, particularly those based on GANs or diffusion models, can have millions of parameters, making naive weight transfer bandwidth-intensive. Federated super-resolution employs communication-efficient strategies:

• Gradient compression via quantization or sparsification reduces update sizes by 100x or more
• Federated distillation exchanges only model outputs on a shared proxy dataset rather than full weights
• Split learning partitions the model so only intermediate activations, not raw data, cross institutional boundaries These techniques make federated super-resolution feasible even across hospitals with limited network infrastructure.

Even sharing model updates can theoretically leak information about training data through membership inference or model inversion attacks. Federated super-resolution integrates differential privacy (DP) by clipping gradients and adding calibrated Gaussian noise during local training. This provides a mathematically provable bound on the privacy loss (ε, δ) for any individual patient scan. While DP introduces a trade-off between privacy and enhancement fidelity, advanced techniques like DP-SGD with adaptive clipping minimize the accuracy penalty, making privacy-preserving super-resolution viable for clinical deployment.

The foundational paradigm for collaboratively optimizing inverse problem solvers across institutions. While super-resolution enhances existing images, reconstruction learns to map raw sensor data directly to diagnostic-quality outputs.

• Trains on k-space data (MRI) or sinograms (CT) without sharing raw acquisition signals
• Enables joint development of Deep Learning Reconstruction (DLR) algorithms
• Addresses the full pipeline from sensor to high-resolution diagnostic image

A critical preprocessing step that learns a common feature space across heterogeneous scanners and protocols. Super-resolution models trained on harmonized data generalize better across institutions.

• Mitigates domain shift caused by vendor-specific reconstruction kernels
• Learns style transfer functions without pooling patient data
• Essential for standardizing inputs before resolution enhancement across sites

The process of adapting a global super-resolution model to a local hospital's specific data distribution. Critical because scanner characteristics and patient demographics vary significantly across sites.

• Addresses covariate shift in imaging protocols
• Enables personalized model performance without sharing local target domain data
• Uses techniques like federated transfer learning and multi-task learning

A closely related collaborative technique that trains models to remove noise artifacts from low-dose CT or fast-acquisition MRI scans. Often paired with super-resolution to jointly enhance quality.

• Enables low-radiation protocols while maintaining diagnostic clarity
• Trains on noisy-clean image pairs without centralizing either
• Complements super-resolution in joint image restoration pipelines

Decentralized training of algorithms to compensate for patient motion artifacts in MRI or PET scans. Motion blur degrades effective resolution, making this a natural companion to super-resolution.

• Learns from diverse motion patterns across institutions
• Addresses respiratory and cardiac motion without sharing corrupted data
• Improves input quality before super-resolution enhancement

Collaborative training of models to suppress metal artifacts, beam hardening, and other imaging distortions. These artifacts obscure fine anatomical details that super-resolution aims to recover.

• Leverages diverse scanner data without sharing artifact-ridden images
• Critical for post-surgical imaging where implants cause distortion
• Works synergistically with super-resolution in image restoration stacks