heady-federated-brain

Cross-environment orchestration coordinates federated learning across Cloudflare Workers AI, Colab Pro+ runtimes, and Cloud Run origins using phi-weighted aggregation and secure additive secret sharing. It triggers multi-round training without exposing raw data.

Phi-weighted federated averaging is a privacy-preserving aggregation method for distributed training. It combines secure additive secret sharing with differential privacy noise calibration to update models collaboratively without exposing raw edge data.

To start federated learning with secure aggregation, register at least three distributed edge nodes and trigger a federation round start. The system manages rounds, model versioning, and convergence monitoring automatically.

Yes, the federated learning approach supports differential privacy guarantees. It applies DP noise calibration during the phi-weighted aggregation process to ensure privacy-preserving collaborative model training across distributed nodes.

Fibonacci-style checkpoints manage model versioning during multi-round training by enabling safe rollbacks and auditing. They work alongside convergence monitoring to track federated learning progress across distributed edge nodes.

Yes, secure aggregation enables distributed training without exposing raw data. It uses additive secret sharing and differential privacy noise calibration across edge nodes to ensure privacy-preserving collaboration.