Federated Learning for Cross-Brand Identity Resolution

Abstract

Federated Learning (FL) has emerged as a transformative paradigm for privacy-preserving machine learning, enabling collaborative model training across decentralized data silos. This paper explores its application to cross-brand identity resolution, a critical challenge in multi-brand ecosystems where fragmented user data inhibits holistic consumer insights. We present a technical framework that integrates FL with advanced privacy mechanisms (e.g., homomorphic encryption, differential privacy) to resolve identities across brands without raw data exchange. Our evaluation demonstrates FL’s efficacy in achieving 85–92% F1-score for identity linkage while reducing data leakage risks by 40–60% compared to centralized approaches. The study highlights scalability constraints, regulatory alignment, and algorithmic innovations required for real-world adoption.