The federated AI blog:
From model exploration to evaluation
Guides, articles, and customer stories on secure AI collaboration
and model discovery
LLM Benchmarking: How to Evaluate Models on Your Data
A model that scores 89% on MMLU benchmark and falls apart on your production data is not a good model. It has overfit to the benchmark. Public benchmark leaderboards are a useful first proxy — they tell you which models are worth a closer look.
Drug Discovery Data: The Access Problem No One Fixes
Drug discovery data is abundant, but pharma R&D teams still can't reach the cohorts that matter. The problem isn't AI. It's the access architecture.
19 Real-World Federated Learning Applications (2026)
Real-world federated learning applications across healthcare, finance, manufacturing, and more — with live examples from the tracebloc platform.
Your AI paper is behind glass — when peer review becomes peer reading
You do serious research. You work with real data — sensitive data that you’re legally, ethically, and contractually obligated to protect. You train your model, run your experiments, document your methodology carefully, and publish.
AI Vendor Evaluation: You Decided to Buy AI. Now What?
Most AI vendor evaluations fail. Discover how to benchmark vendors on your data, measure real performance, and avoid costly mistakes.
Federated Learning in Healthcare: The Implementation Guide
Explore real-world federated learning use cases in healthcare—from retinal disease screening to cardiovascular risk prediction.