SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks

• URL: http://arxiv.org/abs/2608.10144v1
• Date: Mon, 10 Aug 2026 18:59:25 GMT
• Title: SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks
• Abstract summary: SeFoRA is a sketch-aggregated federated LoRA algorithm in which each client transmits a linear sketch of its local updates.<n>SeFoRA alleviates the bilinear mismatch, and allows for aggregation in a small subspace of the full model.<n>We prove convergence to a neighborhood of the first-order stationary point at rate $cO(1/T)$ for the rank-homogeneous setting.
• Score: 15.20977008792474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider federated parameter efficient fine-tuning of large neural networks with low-rank adaptation (LoRA,~Hu et al.\ 2022). Combining LoRA with federated PEFT introduces challenges absent from either setting alone: clients may use different LoRA ranks, making their factor matrices dimension-incompatible, and factor-wise averaging suffers from a bilinear mismatch. We propose SeFoRA, a sketch-aggregated federated LoRA algorithm in which each client transmits a linear sketch of its local updates, enabling direct aggregation at the federator. As a result, SeFoRA alleviates the bilinear mismatch, and allows for aggregation in a small subspace of the full model. We introduce a rank-homogeneous version called SeFoRA-Ho which allows for direct adapter aggregation in this setting. We prove convergence to a neighborhood of the first-order stationary point at rate $\cO(1/T)$ for the rank-homogeneous setting. Numerical experiments on fine-tuning RoBERTa-Large on GLUE datasets show how our algorithms outperform the state-of-the-art.

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