  Healthcare IoMT systems are becoming intelligent, miniaturized, and more
integrated into daily life. As for the distributed devices in the IoMT,
federated learning has become a topical area with cloud-based training
procedures when meeting data security. However, the distribution of IoMT has
the risk of protection from data poisoning attacks. Poisoned data can be
fabricated by falsifying medical data, which urges a security defense to IoMT
systems. Due to the lack of specific labels, the filtering of malicious data is
a unique unsupervised scenario. One of the main challenges is finding robust
data filtering methods for various poisoning attacks. This paper introduces a
Federated Data Sanitization Defense, a novel approach to protect the system
from data poisoning attacks. To solve this unsupervised problem, we first use
federated learning to project all the data to the subspace domain, allowing
unified feature mapping to be established since the data is stored locally.
Then we adopt the federated clustering to re-group their features to clarify
the poisoned data. The clustering is based on the consistent association of
data and its semantics. After we get the clustering of the private data, we do
the data sanitization with a simple yet efficient strategy. In the end, each
device of distributed ImOT is enabled to filter malicious data according to
federated data sanitization. Extensive experiments are conducted to evaluate
the efficacy of the proposed defense method against data poisoning attacks.
Further, we consider our approach in the different poisoning ratios and achieve
a high Accuracy and a low attack success rate.
