Model divergence occurs when aggregating updates from devices with wildly different data distributions, resulting in a global model that performs poorly for everyone. This happens because real edge devices (like fitness trackers from different people or sensors in different environments) often have heterogeneous data that violates the assumption of statistical similarity across devices.
Model divergence occurs when aggregating updates from devices with wildly different data distributions, resulting in a global model that performs poorly for everyone. This happens because real edge devices (like fitness trackers from different people or sensors in different environments) often have heterogeneous data that violates the assumption of statistical similarity across devices.
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Model divergence occurs when aggregating updates from devices with wildly different data distributions, resulting in a global model that performs poorly for everyone. This happens because real edge devices (like fitness trackers from different people or sensors in different environments) often have heterogeneous data that violates the assumption of statistical similarity across devices.
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