Abstract: In the landscape of distributed intelligent systems, achieving a harmonious balance between data privacy and system efficiency remains a paramount challenge. While existing blockchain-based federated learning approaches offer enhanced security, they frequently encounter critical bottlenecks related to static resource allocation, excessive system overhead, and vulnerability to malicious participants. We propose a novel blockchain federated learning framework (FedPPB). FedPPB integrates the Particle Swarm Optimization (PSO) for dynamic role allocation and the Paillier homomorphic encryption for robust privacy preservation. The main idea of FedPPB is that PSO adaptively adjusts the number of worker, validator, and miner nodes by leveraging a multi-objective fitness function. Meanwhile, the model accuracy, verification latency, and block generation time are concurrently optimized. For security, worker nodes employ the Paillier algorithm to encrypt all parameter updates before transmission. Subsequently, these parameters are decrypted and validated by validator nodes to ensure integrity. Finally, miner nodes finalize the process by generating immutable blocks to update the global model. The security of this architecture is rigorously established through theoretical analysis. Comprehensive experiments on the MNIST, Fashion-MNIST, and CIFAR-10 datasets demonstrate that FedPPB significantly outperforms the existing methods, particularly in adversarial environments. Even when confronted with malicious node proportions of 15% and 25%, the proposed framework exhibits superior model accuracy and exceptional robustness, providing a sophisticated and practical solution for building secure, efficient, and adaptive decentralized intelligent systems.