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Federated leaning (FL) has been frequently used in various field of studiesand businesses. Traditional centralized FL systems suffer from serious issues.To address these concerns, decentralized federated learning (DFL) systems havebeen introduced in recent years in which with the help of blockchains, try toachieve more integrity and efficiency. On the other hand, privacy-preserving isan uncovered part of these systems. To address this, and also scaling theblockchain-based computations, we propose a zero knowledge proof (ZKP) basedaggregator (zkDFL) that allows clients to share their large-scale modelparameters with a trusted centralized server without revealing their individualdata to other clients. We utilize blockchain technology to manage theaggregation algorithm via smart contracts. The server performs a ZKP algorithmto prove to the clients that the aggregation is done according to the acceptedalgorithm. The server can also prove that all inputs of clients have been used.We evaluate our measure through a public dataset about wearable internet ofthings. As demonstrated by numerical evaluations, zkDFL introducesverifiability of correctness of aggregation process and enhances the privacyprotection and scalability of DFL systems, while the gas cost has declinedsignificantly.

zkFDL: An efficient and privacy-preserving decentralized federated learning with zero knowledge proof