Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation

2025/12/16 by Laskurain, Unai, Aguirre-Ortuzar, Aitor, Zurutuza, Urko
Computer Science · #Artificial Intelligence (cs.AI) #Cryptography and Data Security #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #Parallel #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2512.14767

openalex publication_date 2025/12/16 · openalex created_date 2025/12/19 · openalex updated_date 2026/07/29

Abstract

Federated Learning (FL) is an emerging machine learning paradigm that enables multiple parties to collaboratively train models without sharing raw data, ensuring data privacy. In Vertical FL (VFL), where each party holds different features for the same users, a key challenge is to evaluate the feature contribution of each party before any model is trained, particularly in the early stages when no model exists. To address this, the Shapley-CMI method was recently proposed as a model-free, information-theoretic approach to feature valuation using Conditional Mutual Information (CMI). However, its original formulation did not provide a practical implementation capable of computing the required permutations and intersections securely. This paper presents a novel privacy-preserving implementation of Shapley-CMI for VFL. Our system introduces a private set intersection (PSI) server that performs all necessary feature permutations and computes encrypted intersection sizes across discretized and encrypted ID groups, without the need for raw data exchange. Each party then uses these intersection results to compute Shapley-CMI values, computing the marginal utility of their features. Initial experiments confirm the correctness and privacy of the proposed system, demonstrating its viability for secure and efficient feature contribution estimation in VFL. This approach ensures data confidentiality, scales across multiple parties, and enables fair data valuation without requiring the sharing of raw data or training models.

Citations

• VFL-RPS: Relevant Participant Selection in Vertical Federated Learning
• Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework
• A Survey on Contribution Evaluation in Vertical Federated Learning
• Balancing Privacy and Performance for Private Federated Learning Algorithms
• FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning
• Fair and efficient contribution valuation for vertical federated learning
• Efficient Client Contribution Evaluation for Horizontal Federated Learning
• Privacy-Preserving Feature Selection with Secure Multiparty Computation
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
• The Five Factor Model of personality and evaluation of drug consumption risk
• Modeling wine preferences by data mining from physicochemical properties

Related

• Interpret Federated Learning with Shapley Values
• OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning
• Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings
• A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
• Differentially Private Vertical Federated Learning