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Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Lear

Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable. We identify the data-parameter int

https://arxiv.org/abs/2607.14367v1 ↗
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Haobo Zhang
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Jiankun Wang
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Suraj Rajendran
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Weishen Pan
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Lam Tsoi
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Yong Chen
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Fei Wang
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Jiayu Zhou
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arXiv Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning · based on signal ▸

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