Veri-SFL: Privacy-Preserving Verification of Resource Allocation and Data Trustworthiness in Sustainable Federated Learning

Federated Learning (FL) is currently referred to as one of the privacy-enhancing technologies because of its service architecture. However, recent advancements in FL have high-lighted its potential not only as a new framework of privacy but also as a key enabler of sustainable computing, which is expected to minimize the impact of an individual party to further improve the capacity of the machine learning model, energy efficiency, and reliability. For the above requirement of sustainability, resource allocation and trust management in FL are very infrastructural tasks of energy efficiency and reliability. In this paper, we present a framework, called Veri-SFL, to indicate verification for resource allocation and trust measurement in FL. We use trust scores to represent the credibility of each dataset without leaking any privacy, and utilize collaborative zk-SNARKs to verify the trust scores of each local dataset. Then, after verifying the correctness of trust levels, we present a solution to verify whether workers (model owners) are training according to the required distribution ratio by using collaborative zk-SNARKs.