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Paper title
IoT Federated Learning Architecture: A secured and privacy-preserving smart home

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Slowly but steadily, the Internet of Things (IoT) is becoming more and more ubiquitous in our daily life. However, it also brings important security and privacy challenges along with it, especially in a sensitive context such as the smart home. In this position paper, we propose a novel architecture for smart home, called IOTFLA, focusing on the security and privacy aspects, which combines federated learning with secure data aggregation. We hope that our proposition will provide a step forward towards achieving more security and privacy in smart homes. KEYWORDS: Security, Privacy, Machine Learning, Federated Learning, Secure Data

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