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      <description>&lt;p&gt;Federated learning (FL) is a promising paradigm that is gaining grip in the context of privacy-preserving machine learning for edge computing systems. Thanks to FL, several data owners called clients (e.g.,  organizations in cross-silo FL) can collaboratively train a model on their private data, without having to send their raw data to external service providers. FL was rapidly adopted in several thriving applications such as digital healthcare, that is generating the world’s largest volume of data. In healthcare systems, the problems of privacy and bias are particularly important.&lt;/p&gt;</description>
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