This paper addresses the security challenges in Federated Learning (FL) by proposing FLARE (Federated Learning with Autonomous Robust Enhancements), a blockchain-based solution. While FL enables collaborative model training without sharing raw data, existing systems often rely on a centralized server for aggregation, creating vulnerabilities and a single point of failure. FLARE integrates blockchain to decentralize trust, ensuring that model updates are securely validated, recorded, and tamper-resistant. By leveraging a hierarchical network structure, FLARE enhances scalability, robustness, and client security. The proposed method mitigates risks like model poisoning and malicious clients in real-world federated learning systems.