Abstract: Federated learning (FL) is becoming an increasingly popular machine learning paradigm in application scenarios where sensitive data available at various local sites cannot be shared due to privacy protection regulations. In FL, the sensitive data never leaves the local sites and only model parameters are shared with a global aggregator. Nonetheless, it has recently been shown that, under some circumstances, the private data can be reconstructed from the model parameters, which implies that data leakage can occur in FL. In this paper, we draw at...
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Topics: 
Data mining
Computer security