Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices

• URL: http://arxiv.org/abs/2502.10239v1
• Date: Fri, 14 Feb 2025 15:49:02 GMT
• Title: Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices
• Authors: Mohamed Aboelenien Ahmed, Kilian Pfeiffer, Ramin Khalili, Heba Khdr, Jörg Henkel,
• Abstract summary: Fine-tuning Large Language Models (LLMs) on edge devices remains challenging due to high memory, communication, and computational demands.<n>We propose Federated Split-Perturbation Zero-order Optimization (FedSPZO) that divides the network into two blocks, applying a different number of perturbations per block.<n>Our evaluation shows a $2.5 - 7times $ reduction in computation overhead compared to zero-order state of the art techniques in federated learning.
• Score: 11.523328603690945
• License: http://creativecommons.org/licenses/by/4.0/
• 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 Federated Split-Perturbation Zero-order Optimization (FedSPZO) 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 $2.5 - 7\times $ reduction in computation overhead compared to zero-order state of the art techniques in federated learning.

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