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Decentralized learning (DL) enables several devices to assemble deep learning models while keeping their private training data on the device. Rather than uploading the training data and model to the server, cross-silo DL only sends the local gradients gradually to the aggregation server back and forth. Hence, DL can provide privacy training of machine learning. Nevertheless, cross-silo DL lacks the proper incentive mechanism for the clients. Thanks to the blockchain, smart contracts (SCs) can address the concerns by providing immutable data records which are self-executing and tamper-proof to failures. Yet, the records of blockchain transactions are publicly visible, which can leak valuable clients’ information as analytical systems become more sophisticated. We leverage the Monero (XMR) protocols to be adjusted into cross-silo DL transactions over wireless networks to address the issues. Concurrently, we investigate the performance of constructed protocols embedded into blockchain smart contracts. This paper also reports and analyzes an empirical investigation of several privacy preservation techniques in decentralized transactions. Overall, the performance results satisfy the design goals. Our observations fill the current literature gap concerning an up-to-date systematic mapping study, not to mention extensive techniques in preserving privacy for cross-silo DL combined with blockchain.