Federated Machine Learning with Kubernetes
summary
A concrete demo shows federated training of a Keras MNIST-style model wrapped with TensorFlow Federated where 10 simulated clients each train locally and send weight updates to a central aggregator. The pipeline runs on a real Kubernetes simulation with a deployment, load balancer and gRPC endpoints. After repeated aggregation over 20 rounds loss steadily decreases and accuracy rises, proving the setup works end to end for heterogeneous local data distributions.
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chapters (6)
- 0:00Start
- 0:51Machine Learning Systems
- 3:29Centralized Approach
- 4:12State of Machine Learning on the Edge
- 7:01Processing Data on the edge
- 8:59Federated Learning
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