Zhaomin Wu

Vertical Federated Learning (VFL) is a crucial paradigm for training machine learning models on feature-partitioned, distributed data. However, due to privacy restrictions, few public real-world VFL datasets exist for algorithm evaluation, and these represent a limited array of feature distributions. Existing benchmarks often resort to synthetic datasets, derived from arbitrary feature splits from a global set, which only capture a subset of feature distributions, leading to inadequate algorithm performance assessment. This paper addresses these shortcomings by introducing two key factors affecting VFL performance - feature importance and feature correlation - and proposing associated evaluation metrics and dataset splitting methods. Additionally, we introduce a real VFL dataset to address the deficit in image-image VFL scenarios. Our comprehensive evaluation of cutting-edge VFL algorithms provides valuable insights for future research in the field.

As machine learning (ML) has been widely developed in real-world applications, the privacy of ML models draws an increasing concern. In this paper, we study how to forget specific data records from ML models to preserve the privacy of these data. Although some studies propose efficient unlearning algorithms on random forests and extremely randomized trees, Gradient Boosting Decision Trees (GBDT), which are widely used in practice, have not been explored. The efficient unlearning of GBDT faces two major challenges: 1) the training of each tree is deterministic and non-robust; 2) the training of a tree depends on all the previous trees. To solve the first challenge, we propose a robust GBDT-like ML model DeltaBoost that enables efficient and accurate deletion according to our theoretical analysis. For the second challenge, we design a training algorithm for DeltaBoost that minimizes the dependency among trees. Our experiments on five datasets demonstrate that DeltaBoost can remove data records from the trained model efficiently and effectively. Our unlearning approach achieves up to two orders of magnitude speedup compared to retraining GBDT. Besides, DeltaBoost produces competitive performance to existing decision-tree-based ML models.

Federated learning is a learning paradigm to enable collaborative learning across different parties without revealing raw data. Notably, vertical federated learning (VFL), where parties share the same set of samples but only hold partial features, has a wide range of real-world applications. However, most existing studies in VFL disregard the" record linkage”process. They design algorithms either assuming the data from different parties can be exactly linked or simply linking each record with its most similar neighboring record. These approaches may fail to capture the key features from other less similar records. Moreover, such improper linkage cannot be corrected by training since existing approaches provide no feedback on linkage during training. In this paper, we design a novel coupled training paradigm, FedSim, that integrates one-to-many linkage into the training process. Besides enabling VFL in many real-world applications with fuzzy identifiers, FedSim also achieves better performance in traditional VFL tasks. Moreover, we theoretically analyze the additional privacy risk incurred by sharing similarities. Our experiments on eight datasets with various similarity metrics show that FedSim outperforms other state-of-the-art baselines. The codes of FedSim are available at https://github. com/Xtra-Computing/FedSim.