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Core
Federated Learning
Federated learning trains a shared model across many devices or organizations without shipping their raw data to a central server: each party computes updates locally and only the updates get aggregated. It weighs communication cost, data heterogeneity, and privacy leakage against the payoff of training on data that legally or practically cannot be pooled. AI, ML, and GenAI interviews probe it to see whether you can separate 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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