On the Convergence of Zeroth-Order Federated Tuning for Large Language Models

• URL: http://arxiv.org/abs/2402.05926v3
• Date: Mon, 17 Jun 2024 16:00:59 GMT
• Title: On the Convergence of Zeroth-Order Federated Tuning for Large Language Models
• Authors: Zhenqing Ling, Daoyuan Chen, Liuyi Yao, Yaliang Li, Ying Shen,
• Abstract summary: Federated Learning and Large Language Models (LLMs) are ushering in a new era in privacy-preserving natural language processing. Memory-efficient Zeroth-Order Optimization is a synergy we term as FedMeZO. Our study is the first to examine the theoretical underpinnings of FedMeZO in the context of LLMs.
• Score: 36.277423093218275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing. However, the intensive memory requirements for fine-tuning LLMs pose significant challenges, especially when deploying on clients with limited computational resources. To circumvent this, we explore the novel integration of Memory-efficient Zeroth-Order Optimization within a federated setting, a synergy we term as FedMeZO. Our study is the first to examine the theoretical underpinnings of FedMeZO in the context of LLMs, tackling key questions regarding the influence of large parameter spaces on optimization behavior, the establishment of convergence properties, and the identification of critical parameters for convergence to inform personalized federated strategies. Our extensive empirical evidence supports the theory, showing that FedMeZO not only converges faster than traditional first-order methods such as FedAvg but also significantly reduces GPU memory usage during training to levels comparable to those during inference. Moreover, the proposed personalized FL strategy that is built upon the theoretical insights to customize the client-wise learning rate can effectively accelerate loss reduction. We hope our work can help to bridge theoretical and practical aspects of federated fine-tuning for LLMs, thereby stimulating further advancements and research in this area.

Related papers

• Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information [6.767885381740953]
  Federated learning has emerged as a distributed optimization paradigm. We propose a novel modified framework wherein each client locally performs a perturbed gradient step. We show that our algorithm speeds convergence up to a margin of 30 global rounds compared with FedAvg.
  arXiv  Detail & Related papers  (2024-10-07T23:14:05Z)
• SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning [63.93193829913252]
  We propose an innovative METL strategy called SHERL for resource-limited scenarios. In the early route, intermediate outputs are consolidated via an anti-redundancy operation. In the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead.
  arXiv  Detail & Related papers  (2024-07-10T10:22:35Z)
• SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead [75.87007729801304]
  SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead. Experiments show that SpaFL improves accuracy while requiring much less communication and computing resources compared to sparse baselines.
  arXiv  Detail & Related papers  (2024-06-01T13:10:35Z)
• Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark [166.40879020706151]
  This paper proposes a shift towards BP-free, zeroth-order (ZO) optimization as a solution for reducing memory costs during fine-tuning. Unlike traditional ZO-SGD methods, our work expands the exploration to a wider array of ZO optimization techniques. Our study unveils previously overlooked optimization principles, highlighting the importance of task alignment, the role of the forward gradient method, and the balance between algorithm complexity and fine-tuning performance.
  arXiv  Detail & Related papers  (2024-02-18T14:08:48Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)
• Fed-LAMB: Layerwise and Dimensionwise Locally Adaptive Optimization Algorithm [24.42828071396353]
  In the emerging paradigm of federated learning (FL), large amount of clients, such as mobile devices, are used to train on their respective data. Due to the low bandwidth, decentralized optimization methods need to shift the computation burden from those clients to those servers. We present Fed-LAMB, a novel learning method based on a layerwise, deep neural networks.
  arXiv  Detail & Related papers  (2021-10-01T16:54:31Z)
• Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization [69.07420650261649]
  We introduce a novel, simple, and powerful contrastive MI estimator named as FLO. Empirically, our FLO estimator overcomes the limitations of its predecessors and learns more efficiently. The utility of FLO is verified using an extensive set of benchmarks, which also reveals the trade-offs in practical MI estimation.
  arXiv  Detail & Related papers  (2021-07-02T15:20:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.