Jiaqi Wu

TMLR Journal 2026 Journal Article

Incorporating New Knowledge into Federated Learning: Advances, Insights, and Future Directions

• Lixu Wang
• Sun Yinggang
• Yang Zhao
• Jiaqi Wu
• Jiahua Dong
• Ating Yin
• Qinbin Li
• Qingqing Ye

Federated Learning (FL) is a distributed learning approach that allows participants to collaboratively train machine learning models without sharing the raw data. It is rapidly developing in an era where privacy protection is increasingly valued. It is this rapid development trend, along with the continuous emergence of new demands for FL in the real world, that prompts us to focus on a very important problem: How to Incorporate New Knowledge into Federated Learning? The primary challenge here is to effectively and timely incorporate various new knowledge into existing FL systems and evolve these systems to reduce costs, upgrade functionalities, and facilitate sustainable development. In the meantime, established FL systems should preserve existing functionalities during the incorporation of new knowledge. In this paper, we systematically define the main sources of new knowledge in FL, including new features, tasks, models, and algorithms. For each source, we thoroughly analyze and discuss the technical approaches for incorporating new knowledge into existing FL systems and examine the impact of the form and timing of new knowledge arrival on the incorporation process. Unlike prior surveys that primarily catalogue FL techniques under a fixed system specification, we adopt a lifecycle evolution perspective and synthesize methods that enable time-varying integration of new features, tasks, models, and aggregation algorithms while preserving existing functionality. Furthermore, we comprehensively discuss the potential future directions for FL, incorporating new knowledge and considering a variety of factors, including scenario setups, security and privacy threats, and incentives.

EAAI Journal 2026 Journal Article

Multi-modal vehicle trajectory prediction via hierarchical attention and raster-vector maps encoding in unstructured road environments

• Zhifa Chen
• Peng Chen
• Songyue Yang
• Lei Li
• Jiaqi Wu
• Rentao Sun
• Zhangyu Wang
• Guizhen Yu

IJCAI Conference 2025 Conference Paper

Credit Assignment and Fine-Tuning Enhanced Reinforcement Learning for Collaborative Spatial Crowdsourcing

• Wei Chen
• Yafei Li
• Baolong Mei
• Guanglei Zhu
• Jiaqi Wu
• Mingliang Xu

Collaborative spatial crowdsourcing leverages distributed workers' collective intelligence to accomplish spatial tasks. A central challenge is to efficiently assign suitable workers to collaborate on these tasks. Although mainstream reinforcement learning (RL) methods have proven effective in task allocation, they face two key obstacles: delayed reward feedback and non-stationary data distributions, both hindering optimal allocation and collaborative efficiency. To address these limitations, we propose CAFE (credit assignment and fine-tuning enhanced), a novel multi-agent RL framework for spatial crowdsourcing. CAFE introduces a credit assignment mechanism that distributes rewards based on workers' contributions and spatiotemporal constraints, coupled with bi-level meta-optimization to jointly optimize credit assignment and RL policy. To handle non-stationary spatial task distributions, CAFE employs an adaptive fine-tuning procedure that efficiently adjusts credit assignment parameters while preserving collaborative knowledge. Experiments on two real-world datasets validate the effectiveness of our framework, demonstrating superior performance in terms of task completion and equitable reward redistribution.

EAAI Journal 2025 Journal Article

Decorrelating structure via adapters makes ensemble learning practical for Semi-supervised Learning

• Jiaqi Wu
• Junbiao Pang
• Qingming Huang

TMLR Journal 2025 Journal Article

Diffusion-RainbowPA: Improvements Integrated Preference Alignment for Diffusion-based Text-to-Image Generation

• Haoyuan Sun
• Bin Liang
• Bo Xia
• Jiaqi Wu
• Yifei Zhao
• Kai Qin
• Yongzhe Chang
• Xueqian Wang

Although rapidly increasing capabilities of text-to-image (T2I) models have profound implications across various industries, they concurrently suffer from numerous shortcomings, necessitating the implementation of effective alignment strategies with human preference. Diffusion-DPO and SPO have emerged as robust approaches for aligning diffusion-based T2I models with human preference feedback. However, they tend to suffer from text-image misalignment, aesthetic overfitting and low-quality generation. To tackle such matters, we improve the alignment paradigm through a tripartite perspective, which are the calibration enhancement (Calibration Enhanced Preference Alignment), the overfitting mitigation (Identical Preference Alignment, Jensen-Shannon Divergence Constraint) and the performance optimization (Margin Strengthened Preference Alignment, SFT-like Regularization). Furthermore, combining them with the step-aware preference alignment paradigm, we propose the Diffusion-RainbowPA, a suite of total six improvements that collectively improve the alignment performance of Diffusion-DPO. With comprehensive alignment performance evaluation and comparison, it is demonstrated that Diffusion-RainbowPA outperforms current state-of-the-art methods. We also conduct ablation studies on the introduced components that reveal incorporation of each has positively enhanced alignment performance.

AAAI Conference 2023 Conference Paper

Unsupervised Paraphrasing under Syntax Knowledge

• Tianyuan Liu
• Yuqing Sun
• Jiaqi Wu
• Xi Xu
• Yuchen Han
• Cheng Li
• Bin Gong

The soundness of syntax is an important issue for the paraphrase generation task. Most methods control the syntax of paraphrases by embedding the syntax and semantics in the generation process, which cannot guarantee the syntactical correctness of the results. Different from them, in this paper we investigate the structural patterns of word usages termed as the word composable knowledge and integrate it into the paraphrase generation to control the syntax in an explicit way. This syntax knowledge is pretrained on a large corpus with the dependency relationships and formed as the probabilistic functions on the word-level syntactical soundness. For the sentence-level correctness, we design a hierarchical syntax structure loss to quantitatively verify the syntactical soundness of the paraphrase against the given dependency template. Thus, the generation process can select the appropriate words with consideration on both semantics and syntax. The proposed method is evaluated on a few paraphrase datasets. The experimental results show that the quality of paraphrases by our proposed method outperforms the compared methods, especially in terms of syntax correctness.