"Decentralized Low-Rank Fine-Tuning of LLMs"

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https://arxiv.org/abs/2501.15361

The paper addresses the challenge of fine-tuning Large Language Models in decentralized environments where data privacy and communication efficiency are critical. Traditional Federated Learning relies on a central server, creating bottlenecks. This paper explores decentralized fine-tuning for LLMs.

This paper proposes Dec-LoRA, a decentralized algorithm using Low-Rank Adaptation (LoRA). Dec-LoRA enables clients to fine-tune LLMs collaboratively without a central server.

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📌 Dec-LoRA effectively adapts LoRA for decentralized LLM fine-tuning. It removes the central server bottleneck, enabling scalable and privacy-preserving distributed training.

📌 Leveraging LoRA's parameter efficiency, Dec-LoRA drastically reduces communication overhead in decentralized settings. This makes fine-tuning large models feasible on resource-constrained devices.

📌 Dec-LoRA's compatibility with quantization further enhances its practicality. 4-bit quantization maintains performance while significantly lowering communication bandwidth needs in decentralized LLM training.

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Methods Explored in this Paper 🔧:

→ The paper introduces Decentralized Low-Rank Adaptation, named Dec-LoRA.

→ Dec-LoRA is designed for decentralized fine-tuning of LLMs.

→ It leverages the Low-Rank Adaptation (LoRA) technique.

→ In Dec-LoRA, each client locally trains LoRA matrices using its private data.

→ Clients then communicate and aggregate these LoRA parameter updates with their neighbors in a decentralized network.

→ A mixing matrix is used for parameter aggregation among neighboring clients, eliminating the need for a central server.

→ The algorithm operates in communication rounds, with each round consisting of local updates and decentralized parameter aggregation.

→ Dec-LoRA is evaluated with Ring and Erdős-Rényi network topologies to assess its performance in different communication structures.

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Key Insights 💡:

→ Dec-LoRA achieves comparable performance to centralized LoRA fine-tuning.

→ Decentralized fine-tuning of LLMs using LoRA is viable and effective.

→ Dec-LoRA maintains performance even with quantized LoRA (QLoRA), reducing communication overhead.

→ The algorithm shows robustness under data heterogeneity, although performance slightly decreases in non-i.i.d. settings.

→ Network topology and connectivity influence performance; more connected networks generally lead to better accuracy.

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Results 📊:

→ Dec-LoRA achieves comparable average accuracy to centralized LoRA across tasks like QNLI, SST2, MNLI, and QQP, with differences within a narrow range (e.g., Avg. accuracy for LoRA: 91.06 on QNLI vs Dec-LoRA with 10 clients: 90.79 on QNLI).

→ In experiments with 4-bit quantization, Dec-LoRA maintains similar performance to full-precision Dec-LoRA (e.g., for Rank 2 on QNLI, full precision: 90.65 vs 4-bit: 90.35).

→ Under data heterogeneity, performance slightly decreases but remains competitive (e.g., for Rank 2 on QNLI, i.i.d.: 90.44 vs non-i.i.d.: 89.99).