The widespread decentralized applications and Blockchain components significantly boost the security frameworks in many vertical applications and use-cases including different secured payment methods and smart contracts. The integral part of any smart contract is the validation of the stake-holder identity, in general, while ideally being achieved without the third-party involvement. Recent industrial research works introduce the sovereign-identity system, where Blockchain becomes a decentralized component to establish a self-certified identity and to avoid a centralized trust third party. Hence, the classification of distributed transactions with respect to identity validation across several users becomes more challenging, especially because of the massive and sensitive identities that are issued through many users and IoT devices and that are used to validate transactions. In this context, it is important to identify and classify the malicious and non-malicious types of transactions. Our proposed method achieves the target of identity classifications from variety of transaction data. Since different users may have different device usage patterns, the data samples and labels located on any individual device may follow a different distribution, which cannot represent the global data distribution. Therefore, the solution could be bi-focal to compensate the gap. This paper coins the approach of hybridizing the consensus where as to initiate a machine learning mechanism to collect the local data globally through a permission driven and a federated approach. We introduce here a Federated Reinforcement learning to be improvised for distributed independent data as a policy of consortium while binding the proof of consensus more centrally authenticated.