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Core
Federated Learning
Federated learning trains a shared model across many devices or organizations without moving their raw data to a central server: each party computes updates locally and only the updates are aggregated. It trades communication cost, data heterogeneity, and privacy leakage against the benefit of training on data that legally or practically cannot be pooled. Applied AI interviews probe it to see whether you can distinguish the genuine fit (mobile keyboards, multi-hospital models) from the cases where centralizing data or using differential privacy alone is simpler.
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