Align Before Aggregation: Basis-Consistent Federated LoRA under Heterogeneous Ranks
arXiv:2611.07598v1 Announce Type: new Abstract: Federated fine-tuning of large language models (LLMs) under heterogeneous client capacities is increasingly important, and low-rank adaptation (LoRA) with heterogeneous ranks makes it practical. Existing server-side aggregators…
AI Summary & Key Takeaways
- arXiv:2611.07598v1 Announce Type: new Abstract: Federated fine-tuning of large language models (LLMs) under heterogeneous client capacities is increasingly important, and low-rank adaptation (LoRA) with heterogeneous ranks makes it practical
- Existing server-side aggregators…
Align Before Aggregation: Basis-Consistent Federated LoRA under Heterogeneous Ranks
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