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 subspace. We present a complete empirical evaluation on two pretrained models, RoBERTa-large and SmolLM-360M, over HellaSwag in a non-IID 10-client federated setting, across 3 random seeds (42, 43, 4...
| Authors | Neranjan Senarath, Rohit Muralitharan, Sadia Asif |
|---|---|
| Primary category | cs.LG |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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