🤖 AI Summary
To address ambiguous data ownership and opaque benefit distribution in federated learning, this paper proposes a blockchain- and smart-contract-based mechanism for data rights assertion and fair reward allocation. Methodologically, it designs a Solidity smart contract system deployed on an Ethereum local environment, integrating hash-based evidence anchoring, distributed ledger recording, and a quantifiable contribution evaluation model for federated learning participants—enabling tamper-proof on-chain provenance of data contributions and rule-driven, automated reward distribution. The key innovation lies in the first systematic integration of blockchain immutability and smart contract self-execution into the end-to-end governance lifecycle of federated learning, establishing a fully auditable, end-to-end framework covering rights assertion, evidence preservation, and benefit allocation. Experimental results demonstrate the mechanism’s effectiveness in ensuring contribution record integrity, verifiability of distribution logic, and overall system feasibility.
📝 Abstract
Federated learning can solve the privacy protection problem in distributed data mining and machine learning, and how to protect the ownership, use and income rights of all parties involved in federated learning is an important issue. This paper proposes a federated learning data ownership confirmation mechanism based on blockchain and smart contract, which uses decentralized blockchain technology to save the contribution of each participant on the blockchain, and distributes the benefits of federated learning results through the blockchain. In the local simulation environment of the blockchain, the relevant smart contracts and data structures are simulated and implemented, and the feasibility of the scheme is preliminarily demonstrated.