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Ju Ren

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

MSCFL: Model Structure-Aware Clustered Federated Learning for System Heterogeneity and Data Drift

Yang Xu
Xiaowei Wu
Zifeng Xu
Cheng Zhang
Ju Ren
Yaoxue Zhang

Federated Learning (FL) faces significant challenges arising from both data and system heterogeneity. While Clustered Federated Learning (CFL) mitigates data heterogeneity by grouping clients with similar data distributions, it remains vulnerable to system heterogeneity, which can slow convergence due to performance disparities among clients. Moreover, data drift may degrade clustering accuracy and training efficiency over time. In this work, we propose a Model Structure-aware Clustered Federated Learning (MSCFL) framework that simultaneously addresses the issues of data heterogeneity, system heterogeneity, and data drift. MSCFL incorporates model pruning (MP) into the CFL framework to enhance training efficiency under system heterogeneity. To enable this integration, we address the key challenge of performing effective clustering based on heterogeneous, pruned local models with varying structures. To this end, we design a model structure-based similarity computation algorithm to integrate CFL with MP. To effectively address data drift, we propose a dynamic cluster migration strategy that efficiently monitors model structures via Hamming Distance and triggers re-clustering only when necessary. Extensive experimental results show that MSCFL improves the accuracy and convergence speed of cluster models, outperforming traditional CFL in various settings.

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NeurIPS Conference 2025 Conference Paper

Gains: Fine-grained Federated Domain Adaptation in Open Set

Zhengyi Zhong
Wenzheng Jiang
Weidong Bao
Ji Wang
Qi Wang
Guanbo Wang
Yongheng Deng
Ju Ren

Conventional federated learning (FL) assumes a closed world with a fixed total number of clients. In contrast, new clients continuously join the FL process in real-world scenarios, introducing new knowledge. This raises two critical demands: detecting new knowledge, i. e. , knowledge discovery, and integrating it into the global model, i. e. , knowledge adaptation. Existing research focuses on coarse-grained knowledge discovery, and often sacrifices source domain performance and adaptation efficiency. To this end, we propose a fine-grained federated domain adaptation approach in open set (Gains). Gains splits the model into an encoder and a classifier, empirically revealing features extracted by the encoder are sensitive to domain shifts while classifier parameters are sensitive to class increments. Based on this, we develop fine-grained knowledge discovery and contribution-driven aggregation techniques to identify and incorporate new knowledge. Additionally, an anti-forgetting mechanism is designed to preserve source domain performance, ensuring balanced adaptation. Experimental results on multi-domain datasets across three typical data-shift scenarios demonstrate that Gains significantly outperforms other baselines in performance for both source-domain and target-domain clients. Code is available at: https: //github. com/Zhong-Zhengyi/Gains.

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AAAI Conference 2025 Conference Paper

ShotVL: Human-Centric Highlight Frame Retrieval via Language Queries

Wangyu Xue
Chen Qian
Jiayi Wu
Yang Zhou
Wentao Liu
Ju Ren
Siming Fan
Yaoxue Zhang

Existing research on human-centric video understanding typically focuses on analyzing specific moments or entire videos. However, many applications require higher precision at the frame level. In this work, we propose a novel task, BestShot, which aims to locate highlight frames within human-centric videos through language queries. This task requires not only a deep semantic understanding of human actions but also precise temporal localization. To support this task, we introduce the BestShot Benchmark. The benchmark is meticulously constructed by combining human-annotated highlight frames, duration labels and detailed textual descriptions. These descriptions cover three critical elements: (1) Visual content; (2) Fine-grained actions; and (3) Human pose descriptions. Together, these elements provide the necessary precision to identify the exact highlight frames in videos. To tackle this problem, we have collected two distinct datasets: (i) ShotGPT4o Dataset, which is algorithmically generated by GPT-4o and (ii) Image-SMPLText Dataset, which features large-scale and accurate per-frame pose descriptions using PoseScript and existing pose estimation datasets. Based on these datasets, we present a strong baseline model, ShotVL, fine-tuned from InternVL, specifically for BestShot. We highlight the impressive zero-shot capabilities of our model and offer comparative analyses with existing state-of-the-art (SOTA) models. ShotVL demonstrates a significant 64% improvement over InternVL on the BestShot Benchmark and a notable 68% improvement on the THUMOS14 Benchmark, while maintaining SOTA performance in general image classification and retrieval.

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AAAI Conference 2020 Conference Paper

Attention-over-Attention Field-Aware Factorization Machine

Zhibo Wang
Jinxin Ma
Yongquan Zhang
Qian Wang
Ju Ren
Peng Sun

Factorization Machine (FM) has been a popular approach in supervised predictive tasks, such as click-through rate prediction and recommender systems, due to its great performance and efﬁciency. Recently, several variants of FM have been proposed to improve its performance. However, most of the state-of-the-art prediction algorithms neglected the ﬁeld information of features, and they also failed to discriminate the importance of feature interactions due to the problem of redundant features. In this paper, we present a novel algorithm called Attention-over-Attention Field-aware Factorization Machine (AoAFFM) for better capturing the characteristics of feature interactions. Speciﬁcally, we propose the ﬁeldaware embedding layer to exploit the ﬁeld information of features, and combine it with the attention-over-attention mechanism to learn both feature-level and interaction-level attention to estimate the weight of feature interactions. Experimental results show that the proposed AoAFFM improves FM and FFM with large margin, and outperforms state-of-the-art algorithms on three public benchmark datasets.

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TIST Journal 2019 Journal Article

Efficient and Privacy-preserving Fog-assisted Health Data Sharing Scheme

Wenjuan Tang
Ju Ren
Kuan Zhang
Deyu Zhang
Yaoxue Zhang
Xuemin (Sherman) Shen

Pervasive data collected from e-healthcare devices possess significant medical value through data sharing with professional healthcare service providers. However, health data sharing poses several security issues, such as access control and privacy leakage, as well as faces critical challenges to obtain efficient data analysis and services. In this article, we propose an efficient and privacy-preserving fog-assisted health data sharing (PFHDS) scheme for e-healthcare systems. Specifically, we integrate the fog node to classify the shared data into different categories according to disease risks for efficient health data analysis. Meanwhile, we design an enhanced attribute-based encryption method through combination of a personal access policy on patients and a professional access policy on the fog node for effective medical service provision. Furthermore, we achieve significant encryption consumption reduction for patients by offloading a portion of the computation and storage burden from patients to the fog node. Security discussions show that PFHDS realizes data confidentiality and fine-grained access control with collusion resistance. Performance evaluations demonstrate cost-efficient encryption computation, storage and energy consumption.

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