Federated Learning: Training Without Centralizing Data
Federated learning flips the usual pipeline upside down: instead of dragging everyone's data to one server, the model visits the data. Devices or hospitals train locally, send back only their learned updates, and a server averages those updates into a better shared model — raw data never leaves home. This topic covers FedAvg, why non-IID data is the hard part, secure aggregation, differential privacy, communication costs, and when federated learning is (or is not) the right architecture.
01.The Problem: The Data Refuses to Travel
The standard ML recipe is a move-in recipe: gather everyone's data into one warehouse, train there, ship out a model.
Sometimes people cannot or will not move in:
- A hospital's scans are protected by law and by ethics — patient data leaving the building is a non-starter.
- A bank in one country may be legally forbidden from storing customer records on a server in another (data-residency rules; GDPR pushed this onto the design checklist).
- Your phone keyboard knows things about you that you would riot over if Google collected them centrally.
So the question becomes
What if the model is the one that travels, and the data never moves?
That inversion is federated learning (McMahan et al., 2016 — "Communication-Efficient Learning of Deep Networks from Decentralized Data," AISTATS). Instead of copies of data flowing toward one training server, copies of the model flow toward the data. Each participant learns locally and sends back only what it learned — weight updates, not records.
The prize: a shared model trained on billions of examples that literally no single party ever possessed. The catch: everything about the training loop gets slower, messier, and more attackable — which is why the rest of this topic is engineering, not theory.
One Federated Round
One Federated Round
The server broadcasts weights; a sampled subset of clients trains locally and returns masked updates; secure aggregation sums them under noise, the server applies the averaged delta, and the global model advances — data never leaves its origin.
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