Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models (2409.06277v2)

Abstract: LLMs have become indispensable in numerous real-world applications. Unfortunately, fine-tuning these models at scale, especially in federated settings where data privacy and communication efficiency are critical, presents significant challenges. Existing methods often resort to parameter-efficient fine-tuning (PEFT) to mitigate communication overhead, but this typically comes at the cost of model accuracy. To address these limitations, we propose federated full-parameter tuning at scale for LLMs (Ferret), the first first-order method with shared randomness to enable scalable full-parameter tuning of LLMs across decentralized data sources while maintaining competitive model accuracy. Ferret accomplishes this through three aspects: (1) it employs widely applied first-order methods for efficient local updates; (2) it projects these updates into a low-dimensional space to considerably reduce communication overhead; and (3) it reconstructs local updates from this low-dimensional space with shared randomness to facilitate effective full-parameter global aggregation, ensuring fast convergence and competitive final performance. Our rigorous theoretical analyses and insights along with extensive experiments, show that Ferret significantly enhances the scalability of existing federated full-parameter tuning approaches by achieving high computational efficiency, reduced communication overhead, and fast convergence, all while maintaining competitive model accuracy. Our implementation is available at https://github.com/allen4747/Ferret.

Federated Full-Parameter Tuning at Scale for LLMs

The paper under discussion presents an innovative approach to fine-tuning LLMs in federated learning settings with reduced communication overhead. As the deployment of LLMs across distributed networks becomes increasingly common, the challenge of federated tuning, which preserves data privacy while ensuring model performance, is particularly pertinent. The authors propose a method termed "federated full-parameter tuning," which applies a first-order optimization paradigm combined with shared randomness to address this challenge effectively.

The central contribution of this paper is a novel algorithm designed for federated environments where model parameters can reach billions in size. This algorithm achieves reduced communication costs without sacrificing the model's performance. The approach diverges from traditional parameter-efficient fine-tuning (PEFT) strategies by maintaining full-parameter updates while innovating on how these updates are communicated.

Key Aspects of the Proposed Method

1. First-Order Optimization: The authors leverage widely applied first-order methods for local updates on each client. This choice is crucial because it typically requires fewer iterations for the same update convergence compared to zeroth-order methods, which are commonly used in federated settings but are less efficient.
2. Projection into Low-Dimensional Space: Local updates are projected into a low-dimensional space to significantly reduce the communication load. By employing shared randomness, these projections are efficiently reconstructed at a global level.
3. Shared Randomness for Reconstruction: A distinctive aspect of the proposed method is its use of shared randomness for communicating updates. This technique allows for effective global model aggregation while ensuring fast convergence and maintaining model accuracy.

Theoretical and Empirical Insights

The authors provide rigorous theoretical analyses, including unbiased reconstruction and error bounds of their methodology, which demonstrate the potential of their approach to outperform existing full-parameter tuning methods like FedAvg and FedKSeed. The convergence analysis indicates that the communication round complexity is optimized, with empirical results corroborating fast convergence—even significantly outperforming baselines in terms of computational efficiency and reduced communication overhead.

Implications and Future Directions

The implications of this research are profound for both the theoretical understanding and practical deployment of LLMs in federated settings. This approach paves the way for more efficient deployments of LLMs, facilitating better resource utilization in environments such as mobile and edge computing, where communication constraints are significant.

Future developments could explore the integration of this method with adaptive federated learning strategies and the potential application across varying data distributions and heterogeneous environments. Additionally, the balance between local computation cost and communication reduction could be further optimized, which remains an open area for improvement and innovation.

In conclusion, this paper contributes a scalable solution for full-parameter tuning of LLMs in federated settings, combining the strengths of first-order optimization with a novel communication strategy to achieve a delicate balance between computational cost and model performance. This approach not only enhances the scalability and adaptability of federated learning systems but also sets a precedent for future work to build upon.