Subspace-Constrained Federated Learning with Low-Rank Adaptation

Federated low-rank adaptation methods are attractive for fine-tuning large models under communication and privacy constraints, but heterogeneous client data can induce geometric misalignment between local low-rank updates. We study whether this subspace misalignment leads to destructive aggregation and slower convergence in LoRA-based federated learning. We propose a subspace-regularized federated LoRA objective that encourages local client updates to remain close to a shared global reference su

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ethics.ai (21 June 2026), “Subspace-Constrained Federated Learning with Low-Rank Adaptation,” evidence record 725, https://ethics.ai/record/725 (originally published by arXiv).

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