The Internet of Medical Things (IoMT) yields highly sensitive and non-Independent and Identically Distributed (non-IID) data. While blockchain-based decentralized Federated Learning (BFL) eliminates the single-point-of-failure of server-centric Federated Learning (FL), existing proposals remain poorly adapted to resource-constrained IoMT. In addition, their Byzantine filters (e.g., Multi-Krum) frequently misclassify updates under severe non-IID. We present PS-DFL, a blockchain-based framework that balances privacy, security, robustness, and efficiency. PS-DFL introduces a cluster-then-verification pipeline: masked client updates are first partitioned by K-means to respect non-IID structure, then Multi-Krum is applied within clusters for fine-grained vetting. Privacy is provided in two complementary layers: verification-time masking (to hide updates during vetting) and aggregation-time light Differential Privacy (DP). Each client performs l2-clipping and adds calibrated Gaussian noise (DP-SGD) before secure aggregation via Shamir Secret Sharing (SSS). A stake-based policy mitigates stake concentration and encourages honest participation. In the experiments, a non-IID wearables dataset with a 10% label-flipping adversary and 10-50 clients, PS-DFL achieves an accuracy of approximately 86–89% with stable convergence, exhibiting lower variance and higher resilience than previous approaches. Notably, in attack scenarios with varying threat levels, our method exhibits a roughly 1–15% lower attack success rate relative to existing ones. These results indicate that PS-DFL is a practical and robust solution for collaborative intelligent system in sensitive IoMT environments.