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ZorBA: Zeroth-order Federated Fine-tuning of LLMs with Heterogeneous Block Activation

arXiv:2603.04436v1 Announce Type: new Abstract: Federated fine-tuning of large language models (LLMs) enables collaborative tuning across distributed clients. However, due to the large size of LLMs, local updates in federated learning (FL) may incur substantial video random-access memory (VRAM) usage. Moreover, frequent model exchange may lead to significant communication overhead. To tackle these challenges, in this paper we propose ZorBA, a zeroth-order optimization-based federated fine-tuning framework with heterogeneous block activation. ZorBA leverages zeroth-order optimization to eliminate the storage of gradients at the clients by forward passes. ZorBA includes a heterogeneous block activation mechanism in which the central server allocates different subsets of transformer blocks to clients in order to accelerate the convergence rate and reduce the VRAM usage. Furthermore, ZorBA utilizes shared random seeds and the finite differences of gradients in order to reduce the communic

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Chuiyang Meng, Ming Tang, Vincent W. S. Wong · · 1 min read · 10 views
#cs.LG #cs.AI

arXiv:2603.04436v1 Announce Type: new Abstract: Federated fine-tuning of large language models (LLMs) enables collaborative tuning across distributed clients. However, due to the large size of LLMs, local updates in federated learning (FL) may incur substantial video random-access memory (VRAM) usage. Moreover, frequent model exchange may lead to significant communication overhead. To tackle these challenges, in this paper we propose ZorBA, a zeroth-order optimization-based federated fine-tuning framework with heterogeneous block activation. ZorBA leverages zeroth-order optimization to eliminate the storage of gradients at the clients by forward passes. ZorBA includes a heterogeneous block activation mechanism in which the central server allocates different subsets of transformer blocks to clients in order to accelerate the convergence rate and reduce the VRAM usage. Furthermore, ZorBA utilizes shared random seeds and the finite differences of gradients in order to reduce the communication overhead. We conduct theoretical analysis to characterize the effect of block activation decisions on the convergence rate and VRAM usage. To jointly enhance the convergence rate and reduce the VRAM usage, we formulate an optimization problem to optimize the block activation decisions. We propose an $\epsilon$-constraint lexicographic algorithm to solve this problem. Experimental results show that ZorBA outperforms three federated fine-tuning baselines in VRAM usage by up to 62.41% and incurs a low communication overhead.

Executive Summary

This article proposes ZorBA, a novel federated fine-tuning framework for large language models (LLMs) that addresses the challenges of substantial video random-access memory (VRAM) usage and significant communication overhead. ZorBA leverages zeroth-order optimization to eliminate gradient storage at clients, heterogeneous block activation to accelerate convergence and reduce VRAM usage, and shared random seeds and finite differences to minimize communication overhead. Theoretical analysis and optimization techniques are employed to optimize block activation decisions. Experimental results demonstrate ZorBA's superiority over three federated fine-tuning baselines in VRAM usage and communication overhead. This framework has significant implications for collaborative LLM training in resource-constrained environments.

Key Points

  • ▸ ZorBA proposes a zeroth-order optimization-based federated fine-tuning framework for LLMs
  • ▸ ZorBA incorporates heterogeneous block activation to accelerate convergence and reduce VRAM usage
  • ▸ Theoretical analysis and optimization techniques are employed to optimize block activation decisions

Merits

Strength in addressing VRAM usage challenges

ZorBA's zeroth-order optimization and heterogeneous block activation mechanisms effectively reduce VRAM usage and accelerate convergence, making it suitable for resource-constrained environments.

Demerits

Potential complexity in optimization process

The optimization problem to optimize block activation decisions may be complex and computationally expensive, which could limit its practical application.

Expert Commentary

The proposed ZorBA framework is a significant contribution to the field of federated learning for LLMs, addressing key challenges in VRAM usage and communication overhead. However, its practical application may be limited by the potential complexity of the optimization process. Nonetheless, this research has far-reaching implications for collaborative LLM training in resource-constrained environments and highlights the need for developing more efficient federated learning frameworks.

Recommendations

  • ✓ Future research should focus on further optimizing the block activation decisions and developing more efficient optimization techniques to address the potential complexity of the optimization process.
  • ✓ The proposed ZorBA framework should be further evaluated in real-world applications to demonstrate its efficiency and scalability in resource-constrained environments.

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