  Federated learning (FL) has been widely adopted in various fields of study
and business. Traditional centralized FL systems suffer from serious issues. To
address these concerns, decentralized federated learning (DFL) systems have
been introduced in recent years. With the help of blockchains, they attempt to
achieve more integrity and efficiency. However, privacy preservation remains an
uncovered aspect of these systems. To tackle this, as well as to scale the
blockchain-based computations, we propose a zero-knowledge proof (ZKP)-based
aggregator (zkDFL). This allows clients to share their large-scale model
parameters with a trusted centralized server without revealing their individual
data to other clients. We utilize blockchain technology to manage the
aggregation algorithm via smart contracts. The server performs a ZKP algorithm
to prove to the clients that the aggregation is done according to the accepted
algorithm. Additionally, the server can prove that all inputs from clients have
been used. We evaluate our approach using a public dataset related to the
wearable Internet of Things. As demonstrated by numerical evaluations, zkDFL
introduces verifiability of the correctness of the aggregation process and
enhances the privacy protection and scalability of DFL systems, while the gas
cost has significantly declined.
