  Due to the rising awareness of privacy and security in machine learning
applications, federated learning (FL) has received widespread attention and
applied to several areas, e.g., intelligence healthcare systems, IoT-based
industries, and smart cities. FL enables clients to train a global model
collaboratively without accessing their local training data. However, the
current FL schemes are vulnerable to adversarial attacks. Its architecture
makes detecting and defending against malicious model updates difficult. In
addition, most recent studies to detect FL from malicious updates while
maintaining the model's privacy have not been sufficiently explored. This paper
proposed blockchain-based federated learning with SMPC model verification
against poisoning attacks for healthcare systems. First, we check the machine
learning model from the FL participants through an encrypted inference process
and remove the compromised model. Once the participants' local models have been
verified, the models are sent to the blockchain node to be securely aggregated.
We conducted several experiments with different medical datasets to evaluate
our proposed framework.
