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Beijing Institute of Technology
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FedLAB achieves up to 7.53% improvement over existing methods while ensuring that multimodal graph knowledge remains traceable and privacy-preserving.
Achieving comparable performance to full-precision models, BITEMBED slashes storage costs and enhances embedding efficiency with extreme low-bit quantization.
PRISM boosts performance in modality-deficient federated graph learning by intelligently retrieving and integrating missing modalities from the entire federation.
Language models can learn directly from real-world user interactions, boosting performance without human annotations or simulated environments.
