arXiv:2602.23638v3 Announce Type: replace-cross 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 differ
This paper addresses a known technical challenge in federated learning for large language models, indicating ongoing efforts to improve their practical deployment and efficiency.
Improving the stability and accuracy of Federated LoRA is crucial for the efficient and secure fine-tuning of large models on decentralized data, which has implications for data privacy and distributed AI development.
The proposed solution mitigates rotational misalignment, potentially leading to more reliable and efficient federated learning frameworks for large language models.
- · AI developers
- · Organizations with decentralized data
- · Federated learning platforms
More effective distributed training of large language models becomes feasible.
Increased adoption of federated learning for sensitive datasets due to improved performance and stability.
Accelerated development of domain-specific, privacy-preserving AI applications across various industries.
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Read at arXiv cs.AI