面向资源受限企业的可信自适应联邦学习框架，提升安全与效率
TITAN-FedAnil+: Trust-Based Adaptive Blockchain Federated Learning for Resource-Constrained Intelligent Enterprises
- 基于相似性传播的动态聚类聚合，无需预知攻击者数量即可过滤恶意更新
- 在8GB边缘设备上，50轮通信内存开销减少81%，显著降低资源消耗
- 适合物联网、工业智能等边缘计算场景，尤其关注数据安全与算力受限的部署
联邦学习（FL）已成为保护数据隐私的协同智能有效范式，但非独立同分布（non-IID）的数据异质性与去中心化安全威胁仍是重大挑战，尤其在资源受限的企业环境中。本文提出TITAN-FedAnil+，一种基于区块链的可信自适应联邦学习网络，用于智能企业环境。该框架引入基于相似性传播的自适应聚类聚合机制，无需预先知晓攻击者数量即可识别并过滤恶意更新。同时，采用GPU加速向量化提升计算效率，并设计带签名的状态跳变机制实现轻量级区块链重同步。实验结果表明，在50轮通信中，相较于基线框架，8GB边缘设备上的内存开销最多降低81%。结果表明，TITAN-FedAnil+能有效提升安全联邦学习在智能企业环境中的鲁棒性、可扩展性与资源效率。
原文摘要 · Abstract (English)
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.
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