Adaptive Split Learning over Energy-Constrained Wireless Edge Networks

• URL: http://arxiv.org/abs/2403.05158v1
• Date: Fri, 8 Mar 2024 08:51:37 GMT
• Title: Adaptive Split Learning over Energy-Constrained Wireless Edge Networks
• Authors: Zuguang Li, Wen Wu, Shaohua Wu, and Wei Wang
• Abstract summary: Split learning (SL) is a promising approach for training artificial intelligence (AI) models, in which devices collaborate with a server to train an AI model in a distributed manner. In this paper, we design an adaptive split learning (ASL) scheme which can dynamically select split points for devices and allocate computing resource for the server in wireless edge networks. We show that the ASL scheme can reduce the average training delay and energy consumption by 53.7% and 22.1%, respectively, as compared to the existing SL schemes.
• Score: 15.622592459779156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Split learning (SL) is a promising approach for training artificial intelligence (AI) models, in which devices collaborate with a server to train an AI model in a distributed manner, based on a same fixed split point. However, due to the device heterogeneity and variation of channel conditions, this way is not optimal in training delay and energy consumption. In this paper, we design an adaptive split learning (ASL) scheme which can dynamically select split points for devices and allocate computing resource for the server in wireless edge networks. We formulate an optimization problem to minimize the average training latency subject to long-term energy consumption constraint. The difficulties in solving this problem are the lack of future information and mixed integer programming (MIP). To solve it, we propose an online algorithm leveraging the Lyapunov theory, named OPEN, which decomposes it into a new MIP problem only with the current information. Then, a two-layer optimization method is proposed to solve the MIP problem. Extensive simulation results demonstrate that the ASL scheme can reduce the average training delay and energy consumption by 53.7% and 22.1%, respectively, as compared to the existing SL schemes.

Related papers

• 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)
• Accelerating Split Federated Learning over Wireless Communication Networks [17.97006656280742]
  We consider a split federated learning (SFL) framework that combines the parallel model training mechanism of federated learning (FL) and the model splitting structure of split learning (SL) We formulate a joint problem of split point selection and bandwidth allocation to minimize the system latency. Experiment results demonstrate the superiority of our work in latency reduction and accuracy improvement.
  arXiv  Detail & Related papers  (2023-10-24T07:49:56Z)
• 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)
• 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)
• Time-sensitive Learning for Heterogeneous Federated Edge Intelligence [52.83633954857744]
  We investigate real-time machine learning in a federated edge intelligence (FEI) system. FEI systems exhibit heterogenous communication and computational resource distribution. We propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model.
  arXiv  Detail & Related papers  (2023-01-26T08:13:22Z)
• Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks [103.22651843174471]
  This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. We show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations.
  arXiv  Detail & Related papers  (2022-09-21T08:52:51Z)
• 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)
• Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach [79.59560136273917]
  limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL) We first devise a novel FL framework with partial model aggregation (PMA) The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-04-20T19:09:52Z)
• Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity [10.702853653891902]
  Federated learning (FL) has emerged as a popular methodology for distributing machine learning across wireless edge devices. In this work, we consider optimizing the tradeoff between model performance and resource utilization in FL. Our proposed StoFedDelAv incorporates a localglobal model combiner into the FL computation step.
  arXiv  Detail & Related papers  (2021-12-27T22:30:15Z)
• 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)

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.