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Federated learning (FL) has become the next generation of machine learning (ML) by avoiding local data sharing with a central server. While this becomes a major advantage to client-side privacy, it has a trade-off of becoming vulnerable to poisoning attacks and malicious behavior of the central server. As the decentralization of systems enhances security concerns, integrating decentralized defense for the existing FL systems has been extensively studied to eliminate the security issues of FL systems. This paper proposes a decentralized defense approach to FL systems with blockchain technology to overcome the poisoning attack without affecting the existing FL system's performance. We introduce a reliable blockchain-based FL (BCFL) architecture in two different models, namely, Centralized Aggregated BCFL (CA-BCFL) and Fully Decentralized BCFL (FD-BCFL). Both models utilize secure off-chain …