TOPOGUARD-EVOCHAIN: Policy-Aware Federated Learning with Topological Representations and Blockchain-Based Model Lineage

Abstract

Modern decentralized web systems have grown so complex that managing data across them safely has become a serious challenge. This work presents TopoGuard-EvoChain, a new federated learning framework built to support secure and transparent data governance within JavaScript-based distributed environments. The approach blends three key ideas. First, it uses TopoSketch to capture behavioral patterns as compact topological summaries. Second, it embeds governance constraints directly into local training by means of Differentiable Policy Automata. Third, it employs an Evolutionary Secure Aggregator, which relies on blockchain verification to fuse model updates and maintain an auditable lineage. Experiments were conducted on the CICIDS2017 dataset together with synthetic governance metadata across a five-node hybrid federated setup. The framework reached 98.62 percent accuracy, 98.18 percent F1-score, and a 97.56 percent governance-compliance rate, outperforming FedAvg, FedProx, and BlockFL-GovNet. The proposed framework reduced the reported communication cost by 32.6%, and the blockchain layer achieved 98.6 percent verification success with reduced ledger growth. The model also showed 95.6 percent explanation fidelity and 96.4 percent audit-trail reconstruction accuracy, supporting its reliability in traceable decision making. Overall, the study shows that combining topological learning with blockchain-based tracking can significantly strengthen predictive performance and accountability in federated systems.