Federated learning has recently garnered significant attention owing to its extensive application scenarios. Nevertheless, existing unprotected communication mechanisms give rise to two critical challenges: “parameter leakage” and “low communication efficiency.” To address these issues, this paper introduces a Blockchain-based Federated Learning framework, termed FBChain, which is specifically designed to secure and optimize parameter transmission in federated learning. First, FBChain leverages the immutability of blockchain to store the global model and the hash values (hv) of local model parameters, thereby ensuring that the transmitted data cannot be tampered with. Meanwhile, parameter encryption is adopted to safeguard privacy, and data consistency is guaranteed by verifying the correspondence between local parameter hashes and the stored records. Through this design, the problem of parameter leakage is effectively mitigated. Second, a novel consensus mechanism, Proof of Weighted Link Speed (PoWLS), is developed to dynamically select nodes with higher weighted link speeds for global aggregation and block generation. By doing so, the model alleviates the inefficiency of communication that commonly arises in federated learning systems. Finally, experimental evaluations validate the proposed FBChain model, demonstrating its capacity to enhance communication security while significantly improving efficiency in federated learning environments.