Federated Learning

Federated Learning is the distributed machine-learning paradigm where multiple clients (mobile devices, hospitals, banks, edge nodes) collaboratively train a shared model without sharing their raw data — only model updates (gradients or weight deltas) are exchanged with a central coordinator, who aggregates them into a global model. Coined by Google in 2016 (McMahan et al.) and used in production for Gboard keyboard prediction, federated learning has matured into a mainstream privacy-preserving technique with major deployments at Apple (Siri, on-device personalization), Meta, NVIDIA Clara (medical imaging), Owkin (oncology), and the EU's MELLODDY consortium (pharmaceutical drug discovery across 10 competitors). The two main flavors: cross-device federated learning (millions of phones or browsers, each contributing tiny gradient updates) and cross-silo federated learning (a handful of large organizations like hospitals or banks, each contributing substantial updates from their internal datasets). The standard algorithm is FedAvg: each client trains locally for E epochs, sends weight deltas to the server, the server weighted-averages them, and broadcasts the updated global model. Variants include FedProx (regularization for heterogeneous clients), FedSGD (one local step per round), Scaffold (variance reduction), and FedOpt (server-side momentum). The privacy guarantees from federated learning alone are weak — model updates can leak training data via gradient inversion attacks — so production deployments combine federated learning with differential privacy (noise added to updates), secure aggregation (server sees only the sum of updates, not individual contributions), and homomorphic encryption. Frameworks include Flower (the dominant open-source choice, framework-agnostic), TensorFlow Federated, PyTorch FedML, NVIDIA FLARE, and OpenFL (Intel/Linux Foundation). AI governance teams use federated learning when data residency or regulatory constraints prevent centralizing training data but the participants still want to benefit from a shared model.

Federation as the 25-year-old Centralpoint pattern: Centralpoint has federated content and identity across multi-tenant deployments for 25 years — extending that federation pattern to model training is incremental engineering, not a new paradigm. Federation runs on-premise, tokens meter per skill, and federation-aware chatbots deploy through one line of JavaScript.


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