TITAN-FedAnil+: Trust-Based Adaptive Blockchain Federated Learning for Resource-Constrained Intelligent Enterprises

TITAN-FedAnil+ presents a blockchain-based federated learning framework designed to address data privacy and security challenges in resource-constrained enterprise environments. The system uses adaptive clustering and GPU acceleration to filter malicious updates while reducing memory overhead by up to 81%, making secure distributed learning more practical for edge devices.

TITAN-FedAnil+ tackles a critical intersection of federated learning security and computational efficiency that directly impacts enterprise adoption of decentralized AI systems. The framework addresses two persistent problems: detecting and eliminating compromised model updates without pre-knowing attack patterns, and operating within the strict memory constraints of edge devices. This matters because enterprises increasingly need to train AI models collaboratively while protecting sensitive data, yet existing solutions remain computationally prohibitive for real-world deployments.

Federated learning has gained traction as organizations recognize its privacy-preserving advantages, but data heterogeneity and coordinated attacks remain formidable obstacles. The paper's approach using affinity propagation for adaptive clustering represents a practical advancement over rigid, configuration-dependent defenses. By eliminating the need to specify attacker counts beforehand, TITAN-FedAnil+ increases deployment flexibility across varying threat models.

The 81% memory reduction across 50 communication rounds has direct implications for enterprise infrastructure costs and edge deployment viability. Organizations can now run federated learning on consumer-grade 8GB devices rather than specialized hardware, substantially lowering barriers to implementation. This efficiency improvement extends battery life and enables broader participation from IoT devices and edge servers in collaborative learning frameworks.

The framework signals growing maturity in blockchain-federated learning systems, though real-world validation across diverse enterprise datasets remains limited. Future developments should focus on production deployments, latency benchmarks, and adversarial robustness testing against sophisticated attacks. Enterprise adoption hinges on proving these systems scale reliably beyond controlled experimental settings.

• →Adaptive clustering without prior knowledge of attacker counts improves flexibility in detecting malicious model updates
• →GPU-accelerated vectorization and lightweight blockchain resynchronization achieve up to 81% memory savings on edge devices
• →Framework enables federated learning deployment on consumer-grade 8GB devices, reducing enterprise infrastructure costs
• →Addresses critical gap between federated learning's privacy benefits and practical resource constraints in enterprise environments
• →Combines blockchain verification with efficient aggregation to balance security and computational overhead