Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA
arXiv:2602.20492v1 Announce Type: new Abstract: Decentralized federated learning (DFL) based on low-rank adaptation (LoRA) enables mobile devices with multi-task datasets to collaboratively fine-tune a large language model (LLM) by exchanging locally updated parameters with a subset of neighboring devices via wireless connections for knowledge integration.However, directly aggregating parameters fine-tuned on heterogeneous datasets induces three primary issues across the DFL life-cycle: (i) \textit{catastrophic knowledge forgetting during fine-tuning process}, arising from conflicting update directions caused by data heterogeneity; (ii) \textit{inefficient communication and convergence during model aggregation process}, due to bandwidth-intensive redundant model transmissions; and (iii) \textit{multi-task knowledge interference during inference process}, resulting from incompatible knowledge representations coexistence during inference. To address these issues in a fully decentralized
arXiv:2602.20492v1 Announce Type: new Abstract: Decentralized federated learning (DFL) based on low-rank adaptation (LoRA) enables mobile devices with multi-task datasets to collaboratively fine-tune a large language model (LLM) by exchanging locally updated parameters with a subset of neighboring devices via wireless connections for knowledge integration.However, directly aggregating parameters fine-tuned on heterogeneous datasets induces three primary issues across the DFL life-cycle: (i) \textit{catastrophic knowledge forgetting during fine-tuning process}, arising from conflicting update directions caused by data heterogeneity; (ii) \textit{inefficient communication and convergence during model aggregation process}, due to bandwidth-intensive redundant model transmissions; and (iii) \textit{multi-task knowledge interference during inference process}, resulting from incompatible knowledge representations coexistence during inference. To address these issues in a fully decentralized scenario, we first propose a sparse-and-orthogonal LoRA that ensures orthogonality between model updates to eliminate direction conflicts during fine-tuning.Then, we analyze how device connection topology affects multi-task performance, prompting a cluster-based topology design during aggregation.Finally, we propose an implicit mixture of experts (MoE) mechanism to avoid the coexistence of incompatible knowledge during inference. Simulation results demonstrate that the proposed approach effectively reduces communication resource consumption by up to $73\%$ and enhances average performance by $5\%$ compared with the traditional LoRA method.
Executive Summary
This article proposes a novel approach to fine-tune large language models in a decentralized federated learning setting. The authors introduce a sparse-and-orthogonal low-rank adaptation method to address issues of knowledge forgetting, inefficient communication, and knowledge interference. The proposed approach reduces communication resource consumption by up to 73% and enhances average performance by 5% compared to traditional methods. The article highlights the importance of device connection topology and proposes a cluster-based design for improved performance.
Key Points
- ▸ Introduction of sparse-and-orthogonal LoRA to eliminate direction conflicts during fine-tuning
- ▸ Analysis of device connection topology and its impact on multi-task performance
- ▸ Proposal of an implicit mixture of experts mechanism to avoid knowledge interference during inference
Merits
Improved Communication Efficiency
The proposed approach reduces communication resource consumption by up to 73%
Enhanced Performance
The approach enhances average performance by 5% compared to traditional LoRA methods
Demerits
Complexity of Cluster-Based Topology Design
The proposed cluster-based topology design may add complexity to the system
Expert Commentary
The article presents a significant contribution to the field of federated learning, addressing key challenges in decentralized settings. The proposed sparse-and-orthogonal LoRA method and cluster-based topology design demonstrate a deep understanding of the underlying issues. The implicit mixture of experts mechanism is a notable innovation, enabling the avoidance of knowledge interference during inference. However, further research is needed to explore the scalability and robustness of the proposed approach in real-world applications.
Recommendations
- ✓ Further investigation into the scalability of the proposed approach in large-scale decentralized systems
- ✓ Exploration of the application of the proposed method to other domains, such as computer vision and natural language processing
