The irony of modern federated learning: We want to train sophisticated models on edge devices, but those same devices often can't even run the models.

A state-of-the-art language model has 7B+ parameters (~28 GB at 32-bit). An iPhone 15 Pro has 8 GB RAM. This math doesn't work.

Model compression techniques—quantization, pruning, low-rank adaptation—are not just optimizations; they're prerequisites for production FL on edge devices. This post explores cutting-edge compression methods and how Octomil makes them accessible.

The fundamental premise of federated learning is to train a single global model across diverse devices. But what happens when "one size fits all" doesn't fit anyone particularly well?

The personalization dilemma: A global keyboard prediction model trained on millions of devices might be mediocre for everyone—users who text in multiple languages, users with specialized vocabularies (medical, legal), or users with unique writing styles all suffer from a lowest-common-denominator model.

This post explores how personalized federated learning enables Octomil to deliver both collective intelligence and individual adaptation.

"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.