Juli Zhang

Zeyu Yan, Yanfei Yao, Xuanbing Wen et al.

Vertical Federated Learning (VFL) has emerged as a promising paradigm for collaborative model training across distributed feature spaces, which enables privacy-preserving learning without sharing raw data. However, recent studies have confirmed the feasibility of label inference attacks by internal adversaries. By strategically exploiting gradient vectors and semantic embeddings, attackers-through passive, active, or direct attacks-can accurately reconstruct private labels, leading to catastrophic data leakage. Existing defenses, which typically address isolated leakage vectors or are designed for specific types of attacks, remain vulnerable to emerging hybrid attacks that exploit multiple pathways simultaneously. To bridge this gap, we propose Label-Anonymized Defense with Substitution Gradient (LADSG), a unified and lightweight defense framework for VFL. LADSG first anonymizes true labels via soft distillation to reduce semantic exposure, then generates semantically-aligned substitute gradients to disrupt gradient-based leakage, and finally filters anomalous updates through gradient norm detection. It is scalable and compatible with standard VFL pipelines. Extensive experiments on six real-world datasets show that LADSG reduces the success rates of all three types of label inference attacks by 30-60% with minimal computational overhead, demonstrating its practical effectiveness.

Juli Zhang, Zeyu Yan, Jing Zhang et al.

Accurate remote sensing-based crop yield prediction remains a fundamental challenging task due to complex spatial patterns, heterogeneous spectral characteristics, and dynamic agricultural conditions. Existing methods often suffer from limited spatial modeling capacity, weak generalization across crop types and years. To address these challenges, we propose DFYP, a novel Dynamic Fusion framework for crop Yield Prediction, which combines spectral channel attention, edge-adaptive spatial modeling and a learnable fusion mechanism to improve robustness across diverse agricultural scenarios. Specifically, DFYP introduces three key components: (1) a Resolution-aware Channel Attention (RCA) module that enhances spectral representation by adaptively reweighting input channels based on resolution-specific characteristics; (2) an Adaptive Operator Learning Network (AOL-Net) that dynamically selects operators for convolutional kernels to improve edge-sensitive spatial feature extraction under varying crop and temporal conditions; and (3) a dual-branch architecture with a learnable fusion mechanism, which jointly models local spatial details and global contextual information to support cross-resolution and cross-crop generalization. Extensive experiments on multi-year datasets MODIS and multi-crop dataset Sentinel-2 demonstrate that DFYP consistently outperforms current state-of-the-art baselines in RMSE, MAE, and R2 across different spatial resolutions, crop types, and time periods, showcasing its effectiveness and robustness for real-world agricultural monitoring.