Mitigating Data Poisoning Risks in Federated Learning through Blockchain Integrated Verifiable Model Aggregation Protocols

Abstract

The rapid expansion of decentralized machine learning has positioned Federated Learning (FL) as a cornerstone for privacy-preserving artificial intelligence, enabling model training across distributed datasets without centralized data collection. Despite its promise, the architecture remains fundamentally vulnerable to data poisoning attacks, where malicious participants introduce corrupted gradients to degrade model performance or install backdoors. This research explores the integration of blockchain technology and verifiable model aggregation protocols as a systemic defense mechanism against such adversarial threats. We analyze the structural trade-offs between computational overhead and system robustness, arguing that traditional centralized aggregators represent a single point of failure and a significant trust bottleneck. By utilizing a decentralized ledger for the verification of local updates and the implementation of consensus-based aggregation, the proposed framework ensures that only mathematically validated contributions are incorporated into the global model. The discussion extends beyond technical implementation to address broader socio-technical implications, including governance models, infrastructure sustainability, and policy frameworks for cross-institutional data collaboration. Our analysis demonstrates that while blockchain integration increases latency, it provides a necessary foundation for verifiable accountability in high-stakes predictive modeling. This paper concludes by examining the future of resilient socio-technical infrastructures and the regulatory shifts required to support decentralized, robust, and fair machine learning systems in an increasingly adversarial global digital landscape.