FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
arXiv preprint arXiv:2110.06976 (2021)
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FBCC introduces unsupervised continual clustering via a teacher-student forward-backward distillation process that outperforms baselines on clustering accuracy while reducing catastrophic forgetting.
MoRe identifies modular structure in representations themselves to enable principled reuse, alignment, and expansion of modules during continual adaptation on sequential data.
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FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
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Unsupervised Continual Clustering via Forward-Backward Knowledge Distillation
FBCC introduces unsupervised continual clustering via a teacher-student forward-backward distillation process that outperforms baselines on clustering accuracy while reducing catastrophic forgetting.
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MoRe: Modular Representations for Principled Continual Representation Learning on Sequential Data
MoRe identifies modular structure in representations themselves to enable principled reuse, alignment, and expansion of modules during continual adaptation on sequential data.