PriCAF: Privacy-Preserving Contribution Assessment in Federated Learning Before Model Training

摘要

Federated learning (FL) facilitates collaborative model training without requiring participants to share their raw training data directly. Fairly evaluating client contributions is essential to ensure equitable benefit allocation and sustained participation. However, most existing methods cannot assess contribution fairness before FL training completes, leading to inefficient resource utilization. While before-training evaluation schemes exist, they either violate privacy requirements or impose prohibitive computational costs. To address these limitations, we propose PriCAF, an efficient and privacy-preserving contribution assessment in FL before model training. Its key innovation involves generating compact, privacy-preserving reduced datasets that encode class distribution, replacing clients' local datasets for assessment. These reduced datasets are aggregated to approximate the global data distribution, eliminating the need for external reference datasets. Extensive experiments demonstrate that PriCAF achieves higher accuracy than state-of-the-art reference-free baselines (before-training assessment) across diverse settings while achieving a 41× speedup in large-scale FL.