PiPar: Pipeline Parallelism for Collaborative Machine Learning

• URL: http://arxiv.org/abs/2302.12803v2
• Date: Tue, 25 Jun 2024 16:17:27 GMT
• Title: PiPar: Pipeline Parallelism for Collaborative Machine Learning
• Authors: Zihan Zhang, Philip Rodgers, Peter Kilpatrick, Ivor Spence, Blesson Varghese,
• Abstract summary: Collaborative machine learning (CML) techniques have been proposed to train deep learning models across multiple mobile devices and a server. CML techniques are privacy-preserving as a local model that is trained on each device instead of the raw data from the device is shared with the server. We identify idling resources on the server and devices due to sequential computation and communication as the principal cause of low resource utilization.
• Score: 16.131285496487678
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative machine learning (CML) techniques, such as federated learning, have been proposed to train deep learning models across multiple mobile devices and a server. CML techniques are privacy-preserving as a local model that is trained on each device instead of the raw data from the device is shared with the server. However, CML training is inefficient due to low resource utilization. We identify idling resources on the server and devices due to sequential computation and communication as the principal cause of low resource utilization. A novel framework PiPar that leverages pipeline parallelism for CML techniques is developed to substantially improve resource utilization. A new training pipeline is designed to parallelize the computations on different hardware resources and communication on different bandwidth resources, thereby accelerating the training process in CML. A low overhead automated parameter selection method is proposed to optimize the pipeline, maximizing the utilization of available resources. The experimental results confirm the validity of the underlying approach of PiPar and highlight that when compared to federated learning: (i) the idle time of the server can be reduced by up to 64.1x, and (ii) the overall training time can be accelerated by up to 34.6x under varying network conditions for a collection of six small and large popular deep neural networks and four datasets without sacrificing accuracy. It is also experimentally demonstrated that PiPar achieves performance benefits when incorporating differential privacy methods and operating in environments with heterogeneous devices and changing bandwidths.

Related papers

• Efficient Asynchronous Federated Learning with Sparsification and Quantization [55.6801207905772]
  Federated Learning (FL) is attracting more and more attention to collaboratively train a machine learning model without transferring raw data. FL generally exploits a parameter server and a large number of edge devices during the whole process of the model training. We propose TEASQ-Fed to exploit edge devices to asynchronously participate in the training process by actively applying for tasks.
  arXiv  Detail & Related papers  (2023-12-23T07:47:07Z)
• Toward efficient resource utilization at edge nodes in federated learning [0.6990493129893112]
  Federated learning enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. computational resource constraints and network communication can become a severe bottleneck for larger model sizes typical for deep learning applications. We propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices.
  arXiv  Detail & Related papers  (2023-09-19T07:04:50Z)
• Federated Split Learning with Only Positive Labels for resource-constrained IoT environment [4.055662817794178]
  Distributed collaborative machine learning (DCML) is a promising method in the Internet of Things (IoT) domain for training deep learning models. We propose splitfed learning, known as splitfed learning (SFL), is the most suitable for efficient training and testing when devices have limited computational capabilities. We show that SFPL outperforms SFL when resource-constrained IoT devices have only positive labeled data.
  arXiv  Detail & Related papers  (2023-07-25T05:33:06Z)
• In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD [51.04126395480625]
  Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. This work offers a solution by simplifying this coupling and enabling in situ training and inference on heterogeneous clusters.
  arXiv  Detail & Related papers  (2023-06-22T14:07:54Z)
• Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks [58.720142291102135]
  Large-scale machine learning models are bringing advances to a broad range of fields. Many of these models are too large to be trained on a single machine, and must be distributed across multiple devices. We show that maximum parallelisation is sub-optimal in relation to user-critical metrics such as throughput and blocking rate.
  arXiv  Detail & Related papers  (2023-01-31T17:41:07Z)
• Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning [55.198301429316125]
  Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices.
  arXiv  Detail & Related papers  (2022-02-07T15:04:15Z)
• Efficient Device Scheduling with Multi-Job Federated Learning [64.21733164243781]
  We propose a novel multi-job Federated Learning framework to enable the parallel training process of multiple jobs. We propose a reinforcement learning-based method and a Bayesian optimization-based method to schedule devices for multiple jobs while minimizing the cost. Our proposed approaches significantly outperform baseline approaches in terms of training time (up to 8.67 times faster) and accuracy (up to 44.6% higher)
  arXiv  Detail & Related papers  (2021-12-11T08:05:11Z)
• Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC [21.267954799102874]
  Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. We propose a new scheduling policy and power allocation scheme using non-orthogonal multiple access (NOMA) settings to maximize the weighted sum data rate. Simulation results show that the proposed scheduling and power allocation scheme can help achieve a higher FL testing accuracy in NOMA based wireless networks.
  arXiv  Detail & Related papers  (2020-06-21T23:07:41Z)
• Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning [73.82875010696849]
  Machine learning algorithms are deployed at the network edge for training artificial intelligence (AI) models. This paper focuses on the novel joint design of parameter (computation load) allocation and bandwidth allocation.
  arXiv  Detail & Related papers  (2020-03-10T05:52:15Z)

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.