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Weijun Xiao

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AAAI Conference 2026 Conference Paper

Ripple Shapley: Data Influence Attribution in One Federated Training Run

  • Dewen Zeng
  • Wenlong Tian
  • Haozhao Wang
  • Jianfeng Lu
  • Weijun Xiao
  • Zhiyong Xu

Contribution evaluation is essential for incentivizing high-quality data sharing in federated learning (FL), yet existing Shapley-value-based methods are prohibitively expensive and overlook temporal influence propagation. In this paper, we propose Ripple Shapley, a novel attribution framework that enables accurate, real-time data valuation within a single federated training run. Our method decomposes each sample’s impact into an instantaneous drop term and a recursive ripple term, the latter capturing downstream influence via a Jacobian chain over global updates. To scale computation, we introduce a low-rank approximation of the Jacobian product and construct a shared subspace for efficient ripple accumulation. Extensive experiments on CIFAR-10 and MNIST show that Ripple Shapley achieves up to 62× speedup over existing Shapley-based FL methods while maintaining high attribution fidelity, significantly improving efficiency, robustness, and fairness in federated environments. We further demonstrate its effectiveness in dynamic federated learning scenarios and its potential for real-time data pricing.

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EAAI Journal 2025 Journal Article

Context-aware resemblance detection for data deduplication with neural network

  • Xuming Ye
  • Wenlong Tian
  • Yaping Wan
  • Ruixuan Li
  • Weijun Xiao
  • Zhiyong Xu

As the prevalence of cloud storage increases, many individuals and companies prefer outsourcing their data for backup and management. However, this has led to a significant increase in redundancy, decreasing storage utilization and wasting network bandwidth. While conventional resemblance detection methods remove redundancy among similar data by comparing the features extracted from each chunk’s content. However, we observed that small changes between similar data chunks may cause false dissimilarity detection by conventional resemblance detection techniques. This is because features derived solely from the chunk content are highly susceptible to various modification patterns. Fortunately, we have discovered that two chunks are likely to be similar if their surrounding chunks are also similar, a concept we refer to as “chunk-context”. Therefore, we propose a novel chunk-context aware resemblance detection method, called CARD, which includes a network-based chunk-context aware model and an N-sub-chunk shingles-based initial feature extraction strategy. By leveraging the Neural network, it can discover the complex patterns between the chunk-context and chunk content itself. A high-level understanding of the contextual information with chunk content can be synthesized into the representation of a chunk. The primary difference compared with others is that our design can significantly improves the accuracy or efficiency of resemblance detection by considering the chunk-context with chunk content itself. Furthermore, we implemented a CARD prototype and conducted extensive experiments using real workload, demonstrating that CARD can detect up to 75. 03% more redundant data and accelerate the resemblance detection operations by 5. 6 × to 86. 7 × faster than state-of-the-art work.

Details DOI
v2026.09.13