Description: Weighted Contribution in the context of federated learning refers to the impact that updates from individual trained models on devices have on the global model, adjusted by a weight factor. This approach is crucial to ensure that the contributions from each client are integrated fairly and effectively into the central model. The idea is that not all client updates hold the same relevance; for instance, a client with a larger or more representative dataset may have a greater influence on the global model than one with less data. Weighted contribution allows the system to account for these differences, adjusting each client’s updates according to their relative weight. This not only improves the accuracy of the global model but also helps mitigate issues such as data bias, as it ensures that updates from clients with less representative data do not distort the model. In summary, weighted contribution is an essential component of federated learning, as it optimizes how contributions from multiple sources are combined, ensuring a more robust and equitable model.
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