Low-Rank Adaptation (LoRA) merging enables efficient customization of T2I diffusion models; nevertheless, centralized aggregation raises serious privacy concerns. While federated LoRA adaptations mitigate these risks, diffusion models present unique challenges: structural heterogeneity and vulnerability to member inference attacks. To overcome these limitations, we propose Fed-DiffLoRA, a privacy-preserving framework that securely aggregates LoRA adapters. At the client level, we disentangle user-specific adaptations into two orthogonal subspaces: content LoRAs preserving semantic fidelity and style LoRAs encoding stylistic features, isolating sensitive attributes from stylistic components. We design a learnable aggregation operator that dynamically optimizes cross-client style LoRA fusion based on the semantic vectors of clients’ content LoRAs, achieving high-fidelity style blending while suppressing client-identifiable patterns. We also provide theoretical guarantees for convergence and privacy. Extensive experiments validate the effectiveness of the proposed approach, achieving substantial reductions in attack success rates and consistently high stylization fidelity.
Fan Qi, Yue Cheng, Xiaoshan Yang et al.· IEEE Transactions on Image P...· 0 citations
Estimated Time of Arrival (ETA) prediction is a core component of intelligent transportation systems. As traffic congestion patterns become increasingly dynamic in large cities, maintaining high prediction accuracy poses a major challenge for ride-hailing platforms. Existing methods either fail to adapt to irregular traffic patterns and sudden congestion, or suffer from new distributions without disentangling long-term trends from short-term fluctuations, thereby degrading model performance in real-world scenarios. To address this challenge, we propose DSETA, an incrementally updated Dual-Stage ETA prediction framework. Specifically, the continual learning process is divided into inter-day and intra-day stages. We first design the intra-day learning stage, which relies entirely on real-time data to enable dynamic adaptation to short-term traffic patterns caused by events like holidays or accidents. Next, we develop the inter-day learning stage, which leverages aggregated historical data from a short time window to capture knowledge of long-term distribution shifts, such as seasonal trends and traffic network evolution. Subsequently, to prevent catastrophic forgetting and preserve knowledge of regular patterns, we explore a Historical Traffic Knowledge Consolidation module. Finally, we validate DSETA's effectiveness and robustness through extensive offline and online experiments conducted on real-world datasets from DiDi's platform. Online A/B tests across three major cities including Beijing, Wuhan, and Xi'an consistently demonstrated performance gains, achieving MAE reductions of 6.62%, 0.73%, and 2.40% respectively. This framework has been successfully deployed in DiDi's production environment, processing hundreds of millions of daily requests and validating its strong performance in industrial applications.
Yanming Lyu, Yue Cheng, Lingkun Li et al.· Proceedings of the 32nd ACM...· 0 citations