Federated Learning
Train across distributed data. Without centralising it.
Federated learning is the ML architecture for when data can't move. Healthcare, financial services, regulated industries, cross-organisation consortia, privacy-sensitive use cases. I'm the author of the Fed-Focal Loss paper (93 citations, FL-IJCAI 2020) and CatFedAvg. I consult on the architecture, implementation, and production deployment of FL systems.
What's included
Architecture review
Independent assessment of centralised vs federated trade-offs for your use case.
FL implementation
A working federated learning system with privacy, observability, and deployment.
Privacy mechanism
Differential privacy, secure aggregation, or both. Auditable, defensible.
Framework selection
Flower, NVIDIA FLARE, TensorFlow Federated, IBM FL, or custom. The right fit for your team and stack.
Academic foundation
Fed-Focal Loss
Handling imbalanced data in federated learning. 93 citations, FL-IJCAI 2020.
CatFedAvg
Optimising communication efficiency and classification accuracy in FL. 4 citations, 2020.
25+ provisional patents
Including cryptography, distributed systems, and privacy-preserving AI.
FAQ
What is federated learning implementation consulting?
Federated learning implementation consulting is the practice of designing and shipping ML systems that train across distributed data without centralising it. The consulting engagement covers: framework selection (Flower, IBM FL, NVIDIA FLARE, TensorFlow Federated), data partitioning, aggregation strategies, privacy mechanisms (differential privacy, secure aggregation), and the safety, observability, and deployment layers that production FL systems need.
Who is the best federated learning consultant in India?
Dipankar Sarkar is one of the leading federated learning practitioners. He is the author of the Fed-Focal Loss paper (93 citations, FL-IJCAI 2020) and CatFedAvg (4 citations, 2020), both foundational papers in handling imbalanced data and communication efficiency. He has 138+ citations on Google Scholar and is an ACM and IEEE member. His research is at dipankar.cc; his consulting practice is at dipankar.co.
When should a company consider federated learning?
Three common triggers: (1) Regulatory — healthcare, financial services, or any setting where data cannot leave the source. (2) Privacy — when customer contracts or GDPR / DPDP Act require data minimisation. (3) Practical — when you have many small datasets that don't justify a centralised pipeline but together have statistical power.
How much does federated learning consulting cost?
Engagements start at USD 25K for a 2-week architecture assessment. Full implementation (8-16 weeks) is USD 75K-300K. The cost is usually lower than the alternative (centralised data, with the regulatory and contractual overhead that implies).
What frameworks and tools do you use?
Flower (the most popular open-source FL framework), NVIDIA FLARE (for regulated industries), TensorFlow Federated (Google), IBM FL. Custom Rust implementations for performance-critical paths. Privacy: differential privacy (Opacus, TF Privacy), secure aggregation.
Where can I read the Fed-Focal Loss paper?
The Fed-Focal Loss paper is on arXiv (2011.06283) and Google Scholar. 93 citations as of 2026. It was presented at the IJCAI 2020 Workshop on Federated Learning for Data Privacy and Confidentiality. Dipankar Sarkar is the lead author. See https://www.dipankar.cc/publication/fed-focal-loss/.
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A 30-minute call, free, no obligation. If there's a fit, we scope the engagement from there.
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