Busting the Myth: Federated Learning Beyond Labeled Data
The notion that federated learning can't be applied to tasks requiring substantial amounts of labeled data has led to a misconception in the AI community. However, the reality is quite different. By harnessing the power of unlabeled data through knowledge distillation, federated learning can indeed facilitate tasks where labeled data is scarce or expensive to obtain.
Knowledge Distillation: A Game-Changer
Knowledge distillation is a technique that enables a large, complex model (the teacher) to transfer its knowledge to a smaller, simpler model (the student). This process involves training the student model on unlabeled data, allowing it to learn the underlying patterns and relationships present in the data. By leveraging this technique in a federated learning setting, participants can collaborate to distill knowledge from their collective, unlabeled data, effectively creating a shared understanding without the need...
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