India’s AI ecosystem shifts from demos to deployment, with the Digital Personal Data Protection Act of 2023 prompting startups to explore federated learning (FL). FL enables training AI models across institutions without raw data leaving its source, maintaining privacy.
In a country where data is fragmented by geography, institutions, and infrastructure, FL is not just a compliance tool. It is an innovation engine that can unlock collaboration in healthcare, banking, agriculture, telecom, sports, and education.
Healthcare: Collaborative Diagnostics Without Data Pools
Healthcare is perhaps the most natural fit for FL in India. Patient records, diagnostic scans, and pathology data are deeply sensitive, but the demand for collaborative models is immense.
SigTuple, a Bengaluru-based health-tech startup, has explored federated medical imaging models to improve pathology diagnostics across hospitals. Additionally, Qure.ai, an AI radiology company, uses FL techniques to train diagnostic models on data from multiple clinical partners without centralising raw scans.
Moreover, FL aligns closely with India’s National Cancer Grid and the IndiaAI-CATCH programme, which aim to build large-scale oncology AI models. For startups, the incentive is clear that FL enables them to compete in regulated healthcare environments.
NodeOps cofounder Naman Kabra said, “Another approach is FedProx, a well-known algorithm that modifies the local optimisation problem to keep client models from diverging too far from the global model. This helps with convergence in non-IID (non-independent and identically distributed data) settings.”
He also mentioned that startups can look into multi-task learning within an FL framework, where different clients are essentially working on various but related tasks that are being federated together.
Instead of seeing a hospital’s data on a specific disease as an imbalance, we can view it as a specialised dataset that enhances our overall understanding when combined with others, he said.
The goal is also to create systems that are resilient to fragmentation, rather than dependent on uniformity, which is crucial for the success of federated learning on a national scale, Kabra emphasised.
More Use Cases
While the financial sector relies heavily on data, FL offers a solution by consolidating sensitive data and enabling the creation of shared fraud detection and credit scoring models without compromising privacy.
Research shows that FL enhances fraud detection accuracy. While large-scale deployments in India are still emerging, lending startups could leverage FL to develop credit risk models using data from non-banking financial companies (NBFCs) and microfinance institutions. Payment service providers could also collaborate on fraud detection, which is crucial as digital transaction volumes in India surpass 100 billion annually.
With 800 million smartphone users, India is a key testing ground for edge AI. For instance, Google’s Gboard uses FL to personalise predictive text while keeping users’ data private. Indian telecom operators could employ FL for network optimisation and call drop predictions, utilising data from distributed base stations.
India’s diverse datasets across regions and crops can benefit from FL in the agritech industry. Researchers at IIIT-Allahabad have created a federated crop disease detection system that attained 97.25% accuracy while keeping data local.
Sports and Fitness Tech: Training Without Leaking Secrets
The Indian sports-tech market is booming, from IPL analytics to grassroots athlete development. But teams guard their proprietary data closely. FL allows collaboration without compromise.
Spoda AI, led by CEO Vibhu Pillai, applies FL in sports analytics. Pillai told AIM, “The majority of use cases in sports can have separate data sources that are heterogeneous in nature as long as the central model remains the same and is not impacted by local models. This approach would provide more safety, something we ensure at Spoda for our clients.”
Researchers are exploring FL for injury prediction models, co-training across clubs without exposing player health data, wearables and fitness apps, where models update on-device and sync securely, fan engagement platforms, and building recommender systems without centralising personal identities.
Additionally, in Indian education systems, FL offers advantages in terms of privacy, decentralisation, and edge computation. Nonetheless, it also faces challenges such as non-iid data, inconsistent device performance, connectivity issues, and even adversarial threats. Sector-specific academic analysis reinforces that FL has promise, especially for education, but comes with real-world implementation barriers, especially relevant to India’s infrastructure landscape.
“It is particularly important to assess the use case at hand and make sure the data in silos is useful enough for training the central model. If there are extremely contradicting or polarised local AI models, then you run a risk of altering the central AI model,” Pillai added.
Challenges to Overcome
Despite enthusiasm, FL adoption in India faces hurdles such as skewed datasets (e.g., one hospital has more oncology cases than another) and challenges to model convergence. Techniques such as FedProx and personalised federated learning are being explored to address this.
Kabra reframes India’s fragmented data challenge as an advantage: “In India, data isn’t just skewed; it’s fragmented by geography, language, dialect, and socioeconomic strata. This isn’t a bug, but a feature that forces us to develop more robust and adaptable FL techniques.”
He sees this as India’s potential export strength: not just consuming federated models, but building the “picks and shovels”, the tools, MLOps platforms, and orchestration layers for FL worldwide.
However, rural India’s patchy bandwidth makes frequent synchronisation costly. Startups must weigh the extra infrastructure burden of FL against centralised approaches. Convincing institutions to share model updates, even without raw data, remains a cultural and legal hurdle.
As the academic paper warns, “Federated learning in India’s education sector will not succeed unless challenges of infrastructure, heterogeneity, and robustness are addressed in parallel.”
India’s Federated Future
Federated learning is no silver bullet, but it offers a uniquely Indian opportunity. In a country defined by fragmented datasets, vague privacy rules, and enormous data scale, FL is both a necessity and a differentiator.
“At NodeOps, we see this opportunity clearly. The future isn’t about Indian companies consuming data and dev tools; it’s about them building and exporting these tools to the world. We can develop the next-gen MLOps platforms, data orchestration layers, and decentralised compute infrastructure that are purpose-built for fragmented data environments,” Kabra asserts.
The road is long. However, if India embraces its chaotic, heterogeneous data landscape as a testing ground, it could become a significant player in building federated systems that are resilient to fragmentation.
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