Federated LLMs Fine-tuned with Adaptive Importance-Aware LoRA

• URL: http://arxiv.org/abs/2411.06581v1
• Date: Sun, 10 Nov 2024 19:59:54 GMT
• Title: Federated LLMs Fine-tuned with Adaptive Importance-Aware LoRA
• Authors: Yang Su, Na Yan, Yansha Deng,
• Abstract summary: Federated fine-tuning of Large Language Models (LLMs) enables task-specific adaptation across diverse datasets while preserving data privacy. We propose a novel Heterogeneous Adaptive Federated Low-Rank Adaptation (LoRA) fine-tuned LLM framework (HAFL) Our method converges quickly with low communication size, and avoids performance degradation when distributing models to clients.
• Score: 24.871424801066006
• License:
• Abstract: Federated fine-tuning of pre-trained Large Language Models (LLMs) enables task-specific adaptation across diverse datasets while preserving data privacy. However, the large model size and heterogeneity in client resources pose significant computational and communication challenges. To address these issues, in this paper, we propose a novel Heterogeneous Adaptive Federated Low-Rank Adaptation (LoRA) fine-tuned LLM framework (HAFL). To accommodate client resource heterogeneity, we first introduce an importance-based parameter truncation scheme, which allows clients to have different LoRA ranks, and smoothed sensitivity scores are used as importance indicators. Despite its flexibility, the truncation process may cause performance degradation. To tackle this problem, we develop an importance-based parameter freezing scheme. In this approach, both the cloud server and clients maintain the same LoRA rank, while clients selectively update only the most important decomposed LoRA rank-1 matrices, keeping the rest frozen. To mitigate the information dilution caused by the zero-padding aggregation method, we propose an adaptive aggregation approach that operates at the decomposed rank-1 matrix level. Experiments on the 20 News Group classification task show that our method converges quickly with low communication size, and avoids performance degradation when distributing models to clients compared to truncation-based heterogeneous LoRA rank scheme. Additionally, our adaptive aggregation method achieves faster convergence compared to the zero-padding approach.

Related papers

• LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement [5.162783756846019]
  Foundation models (FMs) achieve strong performance across diverse tasks with task-specific fine-tuning. Low-Rank Adaptation (LoRA) methods like Low-Rank Adaptation (LoRA) reduce this cost by introducing low-rank matrices for tuning fewer parameters. LoRA-FAIR maintains computational and communication efficiency, yielding superior performance over state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-22T14:19:01Z)
• IterIS: Iterative Inference-Solving Alignment for LoRA Merging [14.263218227928729]
  Low-rank adaptations (LoRAs) are widely used to fine-tune large models across various domains for specific downstream tasks. LoRA merging presents an effective solution by combining multiple LoRAs into a unified adapter while maintaining data privacy.
  arXiv  Detail & Related papers  (2024-11-21T19:04:02Z)
• Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models [5.1613368481802455]
  Low-Rank Adaptation (LoRA) is a popular technique for efficient fine-tuning of foundation models. We propose Federated Exact LoRA, or FedEx-LoRA, which adds a residual error term to the pretrained frozen weight matrix. Our approach achieves exact updates with minimal computational and communication overhead, preserving LoRA's efficiency.
  arXiv  Detail & Related papers  (2024-10-12T08:22:44Z)
• Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation [58.288682735160585]
  Low-Rank Adaptation (LoRA) is a popular technique for finetuning models. LoRA often under performs when compared to full- parameter fine-tuning. We present a framework that rigorously analyzes the adaptation rates of LoRA methods.
  arXiv  Detail & Related papers  (2024-10-10T18:51:53Z)
• FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations [39.88985198467528]
  We introduce a new approach called FLORA that enables federated fine-tuning on heterogeneous LoRA adapters. Our approach is noise-free and seamlessly supports heterogeneous LoRA adapters.
  arXiv  Detail & Related papers  (2024-09-09T18:21:23Z)
• SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning [63.93193829913252]
  We propose an innovative METL strategy called SHERL for resource-limited scenarios. In the early route, intermediate outputs are consolidated via an anti-redundancy operation. In the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead.
  arXiv  Detail & Related papers  (2024-07-10T10:22:35Z)
• Towards Federated Low-Rank Adaptation of Language Models with Rank Heterogeneity [12.515874333424929]
  We observe that heterogeneous ranks among clients lead to unstable performance. Our analysis attributes this instability to the conventional zero-padding aggregation strategy. We propose a replication-based padding strategy that better retains valuable information from clients with high-quality data.
  arXiv  Detail & Related papers  (2024-06-25T11:49:33Z)
• LoRA-Ensemble: Efficient Uncertainty Modelling for Self-attention Networks [52.46420522934253]
  We introduce LoRA-Ensemble, a parameter-efficient deep ensemble method for self-attention networks. By employing a single pre-trained self-attention network with weights shared across all members, we train member-specific low-rank matrices for the attention projections. Our method exhibits superior calibration compared to explicit ensembles and achieves similar or better accuracy across various prediction tasks and datasets.
  arXiv  Detail & Related papers  (2024-05-23T11:10:32Z)
• Sparse Low-rank Adaptation of Pre-trained Language Models [79.74094517030035]
  We introduce sparse low-rank adaptation (SoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process. Our approach strengthens the representation power of LoRA by initializing it with a higher rank, while efficiently taming a temporarily increased number of parameters. Our experimental results demonstrate that SoRA can outperform other baselines even with 70% retained parameters and 70% training time.
  arXiv  Detail & Related papers  (2023-11-20T11:56:25Z)
• Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning [55.49099125128281]
  We propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation. We show that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
  arXiv  Detail & Related papers  (2023-11-03T13:34:44Z)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.