The simulation visualizes a federated learning system where a central server orchestrates repeated rounds of broadcast, local training, upload, and aggregation across a ring of client devices, with a live accuracy metric climbing toward convergence.
Choose the number of client devices, adjust the round speed, and watch particles travel outward as the model is broadcast, clients pulse while training locally (some dimming as they drop out), then particles flow back in as the server aggregates and its color shifts to mark an improved global model.
Client count, round speed, play/pause, and rebuild controls
Real-world federated learning is used across hundreds of millions of phones to improve keyboard next-word prediction, and no individual's typed messages ever leave their device.
The simulation visualizes a federated learning system where a central server orchestrates repeated rounds of broadcast, local training, upload, and aggregation across a ring of client devices, with a live accuracy metric climbing toward convergence.
The simulation visualizes a federated learning system where a central server orchestrates repeated rounds of broadcast, local training, upload, and aggregation across a ring of client devices, with a live accuracy metric climbing toward convergence.
Choose the number of client devices, adjust the round speed, and watch particles travel outward as the model is broadcast, clients pulse while training locally (some dimming as they drop out), then particles flow back in as the server aggregates and its color shifts to mark an improved global model.
Real-world federated learning is used across hundreds of millions of phones to improve keyboard next-word prediction, and no individual's typed messages ever leave their device.