12 posts tagged with "federated-learning"

"Your data never leaves your device"—the classic federated learning pitch. While technically true (raw data stays local), this statement masks a subtle reality: model updates can leak private information.

Gradient updates, aggregated statistics, and even model predictions can reveal sensitive training data through reconstruction attacks, membership inference, or model inversion. True privacy in federated learning requires rigorous mathematical guarantees, not just architectural promises.

This post explores the privacy landscape in FL and how Octomil implements provable privacy protections.

The federated learning research landscape is exploding. NeurIPS 2024 alone featured 100+ FL papers. ICML, ICLR, TMLR—every major venue now has substantial FL content.

But there's a chasm between research prototypes and production systems.

Research papers provide algorithms, convergence proofs, and benchmark results on MNIST/CIFAR. Production systems need to handle millions of mobile devices, unreliable networks, Byzantine attackers, GDPR compliance, and 99.9% uptime requirements.

Octomil bridges this gap. This post explains how we translate cutting-edge research into a platform you can pip install and deploy.