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Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices
arXiv:2502.10239v2 Announce Type: replace
Abstract: Federated fine-tuning offers a promising approach for tuning Large Language Models (LLMs) on edge devices while preserving data privacy. However, fine-tuning these models on edge devices remains challenging due to high memory, communication, and computational demands. Zero-order optimization with task alignment provides a potential solution, enabling fine-tuning with inference-level memory requirements but requires a longer convergence time. In this paper, we propose \ac{METHOD} that divides the network into two blocks, applying a different number of perturbations per block in a computationally effective way, achieving faster convergence. Our evaluation shows a $1.6-3\times$ reduction in computation overhead compared to zero-order state of the art techniques in federated learning.
Abstract: Federated fine-tuning offers a promising approach for tuning Large Language Models (LLMs) on edge devices while preserving data privacy. However, fine-tuning these models on edge devices remains challenging due to high memory, communication, and computational demands. Zero-order optimization with task alignment provides a potential solution, enabling fine-tuning with inference-level memory requirements but requires a longer convergence time. In this paper, we propose \ac{METHOD} that divides the network into two blocks, applying a different number of perturbations per block in a computationally effective way, achieving faster convergence. Our evaluation shows a $1.6-3\times$ reduction in computation overhead compared to zero-order state of the art techniques in federated learning.
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