Efficient Federated Split Learning for Large Language Models over Communication Networks

• URL: http://arxiv.org/abs/2504.14667v1
• Date: Sun, 20 Apr 2025 16:16:54 GMT
• Title: Efficient Federated Split Learning for Large Language Models over Communication Networks
• Authors: Kai Zhao, Zhaohui Yang,
• Abstract summary: Fine-tuning pre-trained large language models (LLM) in a distributed manner poses significant challenges on resource-constrained edge devices.<n>We propose FedsLLM, a novel framework that integrates split federated learning with parameter-efficient fine-tuning techniques.
• Score: 14.461758448289908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuning pre-trained large language models (LLM) in a distributed manner poses significant challenges on resource-constrained edge devices. To address this challenge, we propose FedsLLM, a novel framework that integrates split federated learning with parameter-efficient fine-tuning techniques. By leveraging model splitting and Low-Rank Adaptation (LoRA), FedsLLM reduces the computational burden on edge devices. Furthermore, the introduction of a federated server facilitates parallel training and enhances privacy. To accommodate heterogeneous communication conditions and diverse computational capabilities of edge devices, as well as the impact of LoRA rank selection on model convergence and training cost, we formulate a joint optimization problem. The formulated problem jointly optimizes subchannel allocation, power control, model splitting point selection, and LoRA rank configuration, all aimed at minimizing total training delay. An alternating optimization algorithm is developed to efficiently solve this problem and accelerate the training process. Simulation results demonstrate that the proposed FedsLLM framework achieves comparable model accuracy while significantly reducing client-side computational requirements. Furthermore, the proposed resource allocation scheme and adaptive LoRA rank selection strategy notably reduce the training latency compared to conventional approaches.

Related papers

• Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks [7.439160287320074]
  Federated split learning (FedSL) implements collaborative training across the edge devices and the server through model splitting.<n>We propose a lightweight FedSL scheme, that further alleviates the training burden on resource-constrained edge devices.<n>We conduct theoretical analysis to quantify the convergence performance of the proposed scheme.
  arXiv  Detail & Related papers  (2024-12-09T11:43:03Z)
• Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization [7.013344179232109]
  Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data. Current SL algorithms face limitations in training efficiency and suffer from prolonged latency. We propose the Heterogeneous Split Federated Learning framework, which allows resource-constrained clients to train their personalized client-side models in parallel.
  arXiv  Detail & Related papers  (2024-11-21T07:46:01Z)
• Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning [9.900317349372383]
  Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy communication workload on these devices. We propose a two-tier HFEL system, where edge devices are connected to edge servers and edge servers are interconnected through peer-to-peer (P2P) edge backhauls. Our goal is to enhance the training efficiency of the HFEL system through strategic resource allocation and topology design.
  arXiv  Detail & Related papers  (2024-09-29T01:48:04Z)
• FedsLLM: Federated Split Learning for Large Language Models over Communication Networks [30.47242577997792]
  This paper combines low-rank adaptation technology (LoRA) with the splitfed learning framework to propose the federated split learning for large language models (FedsLLM) framework. The proposed algorithm reduces delays by an average of 47.63% compared to unoptimized scenarios.
  arXiv  Detail & Related papers  (2024-07-12T13:23:54Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining [119.89373423433804]
  Area Under Precision-Recall (AUPRC) was introduced as an effective metric. Serverless multi-party collaborative training can cut down the communications cost by avoiding the server node bottleneck. We propose a new ServerLess biAsed sTochastic gradiEnt (SLATE) algorithm to directly optimize the AUPRC.
  arXiv  Detail & Related papers  (2023-08-06T06:51:32Z)
• Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization [82.12796238714589]
  We propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation. We characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. We establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
  arXiv  Detail & Related papers  (2023-05-04T09:26:03Z)
• Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks [44.37047471448793]
  In this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL) We propose an innovative PSL framework, namely, efficient parallel split learning (EPSL) to accelerate model training. We show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy.
  arXiv  Detail & Related papers  (2023-03-26T16:09:48Z)
• Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning [56.125720497163684]
  We propose a hybrid federated split learning framework in wireless networks. We design a parallel computing scheme for model splitting without label sharing, and theoretically analyze the influence of the delayed gradient caused by the scheme on the convergence speed.
  arXiv  Detail & Related papers  (2022-09-02T10:29:56Z)
• Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels [69.18343801164741]
  partitioned edge learning (PARTEL) implements parameter-server training, a well known distributed learning method, in wireless network. We consider the case of deep neural network (DNN) models which can be trained using PARTEL by introducing some auxiliary variables.
  arXiv  Detail & Related papers  (2020-10-08T15:27:50Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

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.