Federated learning is a distributed paradigm that trained large-scale neural network models with the participation of multiple edge nodes and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client–server architecture, which leads to the single-point-of-failure (SPoF) attack, and the random selection of edge devices for model training compromised the accuracy of the model. Furthermore, adversaries try to initiate inference attack i.e., attack on privacy leads to gradient leakage attack. Hence, we proposed a blockchain-based optimized edge node selection and privacy-preserved framework to address the aforementioned issues. We have designed three kinds of smart contracts (1) registration of edge nodes (2) forward bidding to select optimized edge devices for FL model training, and (3) payment settlement and reward smart contracts. Moreover, fully homomorphic encryption with the Cheon, Kim, Kim, and Song (CKKS) method is implemented before transmitting the local model updates to the server. Finally, we evaluated our proposed method on the benchmark dataset and compared it with other state-of-the-art studies. Consequently, we have achieved a higher accuracy and privacy-preserved FL framework with a decentralized nature. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.