FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
arXiv:2602.23638v1 Announce Type: new Abstract: Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data. In practice, however, a discrepancy between the factor-wise averaging used to preserve low rank and the mathematically correct aggregation of local updates can cause significant aggregation error and unstable training. We argue that a major source of this problem is rotational misalignment, arising from the rotational invariance of low-rank factorizations -- semantically equivalent updates can be represented in different latent subspaces across clients since $(B_i R_i)(R_i^\top A_i) = B_i A_i$. When such misaligned factors are averaged directly, they interfere destructively and degrade the global update. To address this issue, we propose FedRot-LoRA, a federated LoRA framework that aligns client updates via orthogonal transformations prior to aggregation. This alignment preserves the semantic update while reducing cros
arXiv:2602.23638v1 Announce Type: new Abstract: Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data. In practice, however, a discrepancy between the factor-wise averaging used to preserve low rank and the mathematically correct aggregation of local updates can cause significant aggregation error and unstable training. We argue that a major source of this problem is rotational misalignment, arising from the rotational invariance of low-rank factorizations -- semantically equivalent updates can be represented in different latent subspaces across clients since $(B_i R_i)(R_i^\top A_i) = B_i A_i$. When such misaligned factors are averaged directly, they interfere destructively and degrade the global update. To address this issue, we propose FedRot-LoRA, a federated LoRA framework that aligns client updates via orthogonal transformations prior to aggregation. This alignment preserves the semantic update while reducing cross-client subspace mismatch, without increasing communication cost or restricting model expressivity. We provide a convergence analysis that examines the aggregation error induced by factor-wise averaging and shows how rotational alignment yields a tighter upper bound on this error. Extensive experiments on natural language understanding and generative tasks demonstrate that FedRot-LoRA consistently outperforms existing federated LoRA baselines across a range of heterogeneity levels and LoRA ranks.
Executive Summary
The article introduces FedRot-LoRA, a novel federated learning framework that addresses the issue of rotational misalignment in federated LoRA. Rotational misalignment arises from the rotational invariance of low-rank factorizations, leading to aggregation error and unstable training. FedRot-LoRA aligns client updates via orthogonal transformations prior to aggregation, preserving semantic updates while reducing cross-client subspace mismatch. Experiments demonstrate the superiority of FedRot-LoRA over existing federated LoRA baselines. The framework's convergence analysis provides a tighter upper bound on aggregation error, making it an attractive solution for large-scale decentralized machine learning applications. FedRot-LoRA's alignment step does not increase communication cost or restrict model expressivity, a significant advantage in the federated learning paradigm.
Key Points
- ▸ FedRot-LoRA addresses rotational misalignment in federated LoRA through orthogonal transformation alignment
- ▸ Alignment preserves semantic updates and reduces cross-client subspace mismatch
- ▸ Convergence analysis provides a tighter upper bound on aggregation error
Merits
Strength in Federated Learning
FedRot-LoRA's alignment step does not increase communication cost or restrict model expressivity, a significant advantage in the federated learning paradigm.
Improved Convergence Analysis
The framework's convergence analysis provides a tighter upper bound on aggregation error, making it an attractive solution for large-scale decentralized machine learning applications.
Demerits
Potential Complexity
The orthogonal transformation alignment step may introduce additional computational complexity, which could impact real-time applications.
Dependence on Low-Rank Factorizations
The effectiveness of FedRot-LoRA relies on the low-rank factorization approach, which may not be suitable for all types of decentralized data.
Expert Commentary
FedRot-LoRA is a significant contribution to the field of decentralized machine learning, addressing a critical issue in federated LoRA. The framework's alignment step and convergence analysis provide a novel approach to mitigating rotational misalignment, making it an attractive solution for large-scale applications. However, the potential complexity and dependence on low-rank factorizations are limitations that require further investigation. The implications of FedRot-LoRA extend beyond the federated learning paradigm, highlighting the need for further research in decentralized machine learning and its applications.
Recommendations
- ✓ Future research should focus on extending the applicability of FedRot-LoRA to other decentralized machine learning frameworks and applications.
- ✓ Investigation into the potential complexity and computational overhead of the orthogonal transformation alignment step is necessary to ensure scalability and efficiency in large-scale applications.