HeimdaLLM is a cloud-assisted federated fine-tuning framework that combines ZOO with Gradient Rectification (ZGR) and reduces memory footprint for client devices, achieves up to 8.8× faster convergence than the baselines, and improves accuracy by up to 10% over state-of-the-art ZOO methods.
He Sun, Jinrui Zhou, Li Li et al.· Proceedings of the 32nd ACM...· 0 citations
CoEM preserves potentially useful source excerpts verbatim in a pending set, allowing subsequent context to clarify their relevance before irreversible compression, and consistently improves long-context reasoning.
Jing-Guang Li, Yebo Wu, Zu-Yi Guo et al.· 0 citations
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, the substantial memory footprint required for model training imposes a critical memory wall on resource-constrained edge devices, severely limiting their participation and compromising the inc...
Yebo Wu, Jing-Guang Li, Chunlin Tian et al.· IEEE Transactions on Paralle...· 1 citation
HeimdaLLM is a cloud-assisted federated fine-tuning framework that combines ZOO with Gradient Rectification (ZGR) and reduces memory footprint for client devices, achieves up to 8.8× faster convergence than the baselines, and improves accuracy by up to 10% over state-of-the-art ZOO methods.
He Sun, Jinrui Zhou, Li Li et al.· Proceedings of the 32nd ACM...· 0 citations
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