Instruction: Discuss the approaches Federated Learning employs to manage the challenges posed by device heterogeneity.
Context: The question is designed to probe the candidate's understanding of the variability in device capabilities within Federated Learning networks and the techniques used to ensure smooth and efficient model training across diverse devices.
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The way I'd approach it in an interview is this: Device heterogeneity is handled by designing around uneven compute, memory, power, and connectivity. That can mean adaptive client selection, variable local workloads, partial participation, smaller update payloads, and...