TITAN-FedAnil+: Trust-Based Adaptive Blockchain Federated Learning for Resource-Constrained Intelligent Enterprises
2026-06-03 • Cryptography and Security
Cryptography and SecurityArtificial IntelligenceMachine Learning
AI summaryⓘ
The authors propose TITAN-FedAnil+, a system designed to help companies collaborate on artificial intelligence while keeping their data private and secure. Their method groups similar updates to spot and ignore harmful ones without knowing how many bad updates exist. They also use special computing tricks to make the process faster and more efficient on small devices. Tests showed their approach uses much less memory and improves safety and speed when many devices work together.
Federated LearningNon-IID dataBlockchainAffinity PropagationMalicious updatesGPU accelerationEdge devicesData privacyAdaptive clusteringResource efficiency
Authors
Muhammad Hadi, Muhammad Jahangir, Talha Shafique, Muhammad Khuram Shahzad
Abstract
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.