Iranian Society of Cryptology The ISC International Journal of Information Security 2008-2045 17 2 2025 07 01 Shapley Value for Federated Learning: A Distributed and Fair Framework 251 259 219572 10.22042/isecure.2025.219572 EN Mohammad Amin Sarzaeem Information Systems and Security Lab. (ISSL), Sharif University of Technology, Tehran, Iran Seyed Reza Hoseini Najarkolaei Information Systems and Security Lab. (ISSL), Sharif University of Technology, Tehran, Iran Mohammad Reza Aref Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran Journal Article 2025 04 23 In a federated learning system, the objective is to train a global model over distributed datasets without centralizing all data on a single unit. This is accomplished by training a local model on the dataset of each data owner and then aggregating these local models to preserve the datasets’ privacy. To incentivize clients to actively engage in the learning process, fairness-aware federated learning techniques can be employed. One such approach involves quantifying the contribution of locally trained models in training the global model by Shapley value (SV) using an additional dataset and rewarding them according to their contributions. However, the calculation of the Shapley value presents a significant challenge due to its high computational complexity. To tackle this issue, our research presents a contribution-based federated learning method that efficiently computes the contribution of each locally trained model by distributing the additional dataset among processing nodes in a private manner and calculating the Shapley value over them. https://www.isecure-journal.com/article_219572_8b750c0dbf493b5bcb1d02c318646fbb.pdf