Federated Learning vs Centralized: 3 Reasons Edge Fails

Federated Learning vs Centralized: 3 Reasons Edge Fails

Federated learning promised to train neural networks across thousands of edge devices without centralizing data. Five years after Google's Gboard implementation, the reality is harsh: for most ML tasks, federated learning still delivers worse accuracy, longer convergence times, and higher operational costs than just shipping data to a central server.

I've benchmarked federated learning setups on Raspberry Pi clusters and Jetson edge nodes. The numbers don't lie. This isn't about theoretical limitations—it's about practical engineering constraints that consistently kill federated projects before they reach production.

Here's what actually happens when you try to replace centralized SGD with federated averaging.

Photo by Google DeepMind on Pexels

The Communication Bottleneck: 10x Slower Than You Think

The core federated learning workflow sounds elegant: train locally on each device, send only model updates to a central server, aggregate gradients, distribute the updated model. No raw data leaves the device.

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