MPCNet: Smart Contract-Based Multiparty Computing Network for Federated Learning

Stepping into the era of big data, with more resources shared, the machine learning algorithms are more likely to derive a better solution, and those complicated computations can be finished in a shorter time. The existing works about multiparty computing mainly focus on how to perform the computation when the involved partners are given, but hardly consider the process during which the partners find each other. In this work, we proposed a framework of the multiparty computing network (MPCNet) for the agents propose and collaborate, where R3 Corda is harnessed to establish a blockchain platform where the convener is able to look for some other partners, and a crowdsourcing process is performed to verify the validity of the conveners proposal and the partners applications. Furthermore, a reward mechanism is proposed in order to motivate the verifiers to participate. Once all the agents joining the computing task are confirmed, they communicate with each other to perform the computing task, following the plan that is mentioned in the proposed smart contract. Experimental results demonstrated the feasibility, usability, and scalability of our proposed approach.