Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning

Federated learning (FL) represents an innovative decentralized paradigm in the field of machine learning, which differs from traditional centralized approaches. It facilitates collaborative model training among multiple participants and transfers only model parameters without directly exchanging raw data to maintain confidentiality. Data valuation for each data provider becomes a critical issue to guarantee the fairness of federated learning by estimating the dataset quality of each data provider based on the contribution to the global model prediction performance. To value datasets in FL, the concept of Shapley value is introduced to estimate the contribution of each dataset to a trained global model by measuring the effects of including and excluding a local model parameter in various combinations of global model parameters. However, the contribution measurement to each dataset performed by an aggregator or certain central component as a verifier becomes irrational as the verifier is under the control of an organization. Thus, this work presents a contribution measurement framework or data valuation with strong fairness, where forged results from the contribution measurement procedure are impossible. The new framework allows every participant (data provider) to verify the results of contribution measurement.