Federated Learning (FL) has emerged as an effective paradigm for collaborative intelligence while preserving data privacy. However, data heterogeneity arising from non-IID distributions and decentralized security threats remain significant challenges, particularly in resource-constrained enterprise environments. This paper presents TITAN-FedAnil+, a Trust-Based Adaptive Network for blockchain-enabled federated learning in intelligent enterprises. The proposed framework introduces affinity propagation-based adaptive clustered aggregation to identify and filter malicious updates without requiring prior knowledge of the number of attackers. In addition, GPU-accelerated vectorization is employed to improve computational efficiency, while a signed state jump mechanism enables lightweight blockchain resynchronization. Experimental results demonstrate substantial reductions in memory overhead, achieving up to 81% savings across 50 communication rounds on constrained 8 GB edge devices compared with the baseline framework. The results indicate that TITAN-FedAnil+ effectively improves robustness, scalability, and resource efficiency for secure federated learning deployments in intelligent enterprise environments.

翻译：联邦学习（FL）已成为一种在保护数据隐私的同时实现协作智能的有效范式。然而，由非独立同分布（non-IID）数据分布引发的数据异质性和去中心化安全威胁仍然是重大挑战，尤其在资源受限的企业环境中尤为突出。本文提出了TITAN-FedAnil+，一种面向智能企业中区块链赋能联邦学习的基于信任的自适应网络。该框架引入了基于亲和传播的自适应聚类聚合机制，无需预先了解攻击者数量即可识别并过滤恶意更新。此外，采用GPU加速向量化技术提升计算效率，同时通过符号状态跳跃机制实现轻量级区块链再同步。实验结果表明，与基线框架相比，该方案显著降低了内存开销：在受限的8 GB边缘设备上运行50轮通信后，内存节省最高达81%。结果表明，TITAN-FedAnil+能够有效提升智能企业环境中安全联邦学习部署的鲁棒性、可扩展性和资源效率。