Instruction: Explain the fundamental differences between Federated Learning and Distributed Learning, focusing on data localization, model training, and privacy aspects.
Context: This question is designed to assess the candidate's understanding of the core concepts of Federated Learning in comparison to Distributed Learning, highlighting their knowledge on how data privacy and model training procedures differ between the two paradigms.
The way I'd explain it in an interview is this: Distributed learning is a broad term for splitting model training across multiple machines to improve speed or scale. Federated learning is more specific: it assumes data is decentralized across clients and often cannot be pooled, so the training design has to account for privacy, heterogeneity, and intermittent participation.
In other words, most distributed training systems assume cooperative infrastructure in one controlled environment. Federated learning assumes fragmented data ownership and much messier client behavior.
What matters in an interview is not only knowing the definition, but being able to connect it back to how it changes modeling, evaluation, or deployment decisions in practice.
A weak answer says both train across many machines and stops there, without distinguishing scale-oriented distributed training from privacy- and ownership-constrained federated training.