In India’s hospitals, data tells very different stories. A Delhi TB clinic may show endless lung scans, while an oncology hospital in Chennai stores tumour-heavy datasets. For artificial intelligence (AI), this abundance is both a blessing and a curse: plenty of data but little coherence.

Patient data is sensitive and very varied across different kinds of hospitals, says Hima Makonahally Pratap, physician advisory board member at the International Journal of Clinical Research. “Each hospital has its own unique set of patients, and data is stored separately, not just for privacy reasons, but because their very nature differs.”

This is where federated learning (FL) comes into play, a framework that enables AI models to learn collaboratively across hospitals, without transferring the underlying patient data. In other words: collaboration without compromise.

The Challenge of Label Skew

One of the biggest hurdles in Indian healthcare data is what researchers call label skew, when disease distributions vary drastically across hospitals.

“When one hospital sees TB patients and another focuses entirely on oncology, you can immediately see how different their data would look,” Pratap notes.

The National TB Prevalence Survey (NPSI) found that TB affects 31% of Indians over 15, with heavy regional variations. This means hospitals in states like Delhi or Tamil Nadu naturally develop specialised datasets. For AI, this causes two main problems:

• Model divergence: Each hospital’s AI model becomes highly specialised, but when aggregated into a global model, the system is pulled in conflicting directions.
  
• Catastrophic forgetting: Knowledge of one disease (like TB) may be overwritten when new data from another speciality (like oncology) is introduced.
  

The result? Instead of converging to a universal solution, AI struggles to serve anyone well.

Yet label skew doesn’t always manifest equally. Dr Zainul Charbiwala, cofounder and CTO of Tricog, a medtech company, observes less skew in cardiac data.

“We currently have about half of our data from urban and the other half from rural healthcare facilities, and we’re not seeing this divide play a big role. The diversity of conditions is so high and the underlying causes are quite similar. The differences don’t stand out too much. In cardiology, ECG is the go-to test everywhere, so the modality is consistent,” he explains.

This nuance highlights a key insight: some medical domains may lend themselves more naturally to federated learning, while others (like radiology) face tougher integration challenges due to differences in equipment, data resolution, and workflows.

From Chaos to Symphony

Pratap uses music to describe the challenge of integrating diverse datasets: A great guitarist and a great pianist may sound wonderful, but if they play at once without coordination, there will only be noise, no music. “Our goal is to preserve their brilliance while creating a symphony.”

Federated learning, combined with smart strategies, aims to create that symphony.

By embedding structured medical knowledge graphs, such as UMLS and SNOMED CT, federated models don’t just learn patterns; they learn relationships. This ensures respiratory conditions, for instance, are weighted more heavily when TB hospitals contribute to lung-related models.

Techniques like FedProx add “gravity” to local models, gently pulling them toward the global model while allowing for speciality-specific variance.

The multilevel aggregation of structured hierarchies in areas of respiratory, cardiovascular, and neurological health, ensures that models evolve within coherent medical contexts before being rolled up into a broader framework.

Tackling the Gaps

One of AI’s biggest limitations in healthcare is the lack of data for rare conditions. Here, synthetic data generation offers a lifeline.

Charbiwala highlights the challenge in cardiology: “Rare conditions are underrepresented. We don’t typically use synthetic data generation from scratch, but instead rely on augmentation, sampling rarer conditions more often and subtly modifying signals to add variation. This avoids bias while still giving the model enough examples to learn from.”

Emerging frameworks like Gen-FedSD can generate realistic medical images based on text prompts, filling critical gaps without exposing patient identities.

India’s healthcare infrastructure is far from uniform. Urban centres boast cutting-edge MRI machines, while rural clinics may rely on older X-ray setups. Network connectivity is another hurdle.

Tricog addresses this with cloud-connected ECG machines. “One element of our design has been to ensure that our devices work even in poor network conditions. We used to have problems circa 2015-16, but with widespread 4G/5G availability, there’s no issue at all today,” says Charbiwala.

For other modalities, federated learning employs hybrid gradient compression (HGC), which smartly reduces the size of updates shared across networks while preserving vital diagnostic signals. This allows even bandwidth-limited rural clinics to participate meaningfully.

Privacy, Regulation, and Trust

Incorporating India’s Digital Information Security in Healthcare Act (DISHA) is central to federated learning adoption.

“We never move raw data, ever. Every model update is auditable, every hospital has full control, and patients have granular consent,” stresses Pratap.

This approach addresses concerns about data misuse, ensuring compliance while fostering public trust.

The potential of FL is evident in India’s healthcare landscape. In Delhi and Bihar, hospitals are using local models to enhance tuberculosis screening and improve pneumonia and COPD detection. Specialised cancer centres in Chennai contribute to global models for early tumour detection without sharing raw scans. Tricog’s ECG platform in Karnataka helps rural clinics identify over 140 cardiac conditions, showcasing FL’s effectiveness in low-resource settings while ensuring data privacy.

The experts agree that India has a unique opportunity to lead. “If we get this right, India could become the blueprint for federated healthcare AI globally. We have diversity, scale, and strong regulatory frameworks. That’s exactly the testbed the world needs,” Pratap reflects.

The key is not to chase glamorous solutions, but to ensure they actually work for the last-mile clinic,” says Charbiwala.

Federated learning is not a silver bullet, but it offers India a pathway to balance privacy, diversity, and innovation in healthcare AI. 

“Each hospital is a brilliant soloist. Federated learning is how we turn them into an orchestra,” concludes Pratap.

The post Orchestrating a Healthcare AI Symphony in India Through Federated Learning  appeared first on Analytics India Magazine.