Authors - Jiwan N. Dehankar, Virendra K. Sharma Abstract - The advanced incorporation of Machine Learning (ML) in blockchain systems present special challenges related to security, scalability, and adversarial robustness. The traditional consensus protocols and aggregation techniques suffer from high latencies, susceptibility to Byzantine node attacks, and inefficiencies in communicating gradients that cripple real-time federated learning on the blockchain networks. On the other hand, existing solutions like PoW (Proof-of-Work) and centralized aggregation do not adapt dynamically to ML workloads and remain vulnerable to adversarial attacks, thus putting the model's integrity into jeopardy and causing grave computational overhead. To mitigate these issues, we present Blockchain-Federated Secure Learning Network (BFSL-Net), an infrastructural framework with a dual purpose of enhancing security and efficacy while providing scalability to blockchain-based ML systems. BFSL-Net is comprised of (1) the Multi-Tiered Hierarchical Consensus Framework (MHC-BCML), (2) Adaptive Byzantine-Resilient Aggregation (ABRA), (3) Secure Adversarial Gradient Masking (SAGM-MLBC) and (4) Hierarchical Graph Neural Network-Based Threat Intelligence (HGNN-TI). BFSL-Net, which brings all the above-mentioned methods into a unified system of real-time threat resistance federated learning. Proposed model shows an adversarial threat mitigation success of 99.6 %, marked 2.8 times improvement in efficiency of ML processing, and a 4.5-fold reduction in blockchain computational overhead, thereby promising secure, scalable ML production environments in the blockchain space.
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