Abstract Federated learning enables training healthcare diagnostic models across multiple decentralized devices containing local private health data samples, without transferring data to a central server, providing privacy‐preserving services for healthcare professionals. However, for a model of a specific field, some medical data from non‐target participants may be included in model training, compromising model accuracy. Moreover, diagnostic queries for healthcare models stored in cloud servers may result in the leakage of the privacy of healthcare participants and the parameters of models. Furthermore, the records of model searching and usage could be tracked causing privacy disclosure risk. To address these issues, we propose a blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for the diagnostic model federated learning in the Internet of Medical Things (BSAEM‐FL). We first adopt fine‐grained model trainer participation policies for federated learning, using the attribute‐based encryption (ABE) mechanism, to realize model accuracy and local data privacy. Then, We employ searchable encryption technology for model training and usage to protect the security of models stored in the cloud server. Blockchain is utilized to implement distributed healthcare models' keyword‐based search and model users' attribute‐based authentication. Lastly, we transfer most of the computational overhead of user terminals in model searching and decryption to edge nodes, achieving lightweight computation of IoMT terminals. The security analysis proves the security of the proposed healthcare scheme. The performance evaluation indicates our scheme is of better feasibility, efficiency, and decentralization.

Sharding blockchains are proposed to solve the scalability problem while maintaining security and decentralization. However, there are still many issues to be solved. First, the member selection and assignment process are not strictly analyzed, which might lead to an increase in the adversary proportion. Second, current intra-shard consensus algorithms are inefficient. Besides, cross-shard transaction processing costs expensive system overhead. Moreover, there is a lack of a formal security framework. In this article, we propose a secure and scalable hybrid consensus (SSHC). First, we propose a fair sharding selection scheme to select committee members, including mining processes and member lists confirmation by a reference committee. Second, a pipelined Byzantine fault tolerance for intra-shard consensus is designed, combining the pipelined technology with threshold signatures. Third, we propose a responsive sharding transaction batch processing mechanism to handle cross-shard transactions, which reduces the number of calls to Byzantine fault tolerance algorithms. Fourth, a secure committee reconfiguration method is designed to update shard members efficiently. Furthermore, we employ a formal security framework to design and analyze a sharding blockchain. For an adversary whose computational power fraction is less than$1/3$, by reasonably setting a corruption parameter and other related parameters, SSHC is proved to achieve consistency and liveness.