We are addressing these challenges using blockchain and smart contract technology. Specifically, we are:
- Using blockchain to create an immutable audit trail for federated models for greater trustworthiness in tracking and proving provenance.
- Enhancing encryption and communication strategies between nodes and federated model to maintain better privacy-preservation.
- Demonstrating the use of smart contracts for governing the business logic of a FedML alliance.
Figure 1 illustrates the envisioned solution. The blocks in the chain contains the references to parameters of ML models, and with each new update of the global model, a new block will be added in the chain. These immutable blocks contains the evolution of a model from the beginning to its final stage. Smart contracts are used to define rules and penalties around an agreement in the same way that a traditional contract does, but also automatically enforce those obligations. In the context of FedML, we are using smart contracts to define the rules for the model training. They will also be used to restrict the number of operations, enable different incentives based on the member's contributions and to define new rules consisting of upcoming requirements. The use of smart contracts will encourage alliance members to contribute independently and earn incentives based on their contributions. It will also help to mitigate errors. The proposed approach of using smart contracts for rule-based model training is unique and has not been tested before within the domain of federated machine learning.