Adaptive Dynamic Pruning for Non-IID Federated Learning

• URL: http://arxiv.org/abs/2106.06921v1
• Date: Sun, 13 Jun 2021 05:27:43 GMT
• Title: Adaptive Dynamic Pruning for Non-IID Federated Learning
• Authors: Sixing Yu, Phuong Nguyen, Ali Anwar, Ali Jannesari
• Abstract summary: Federated Learning(FL) has emerged as a new paradigm of training machine learning models without sacrificing data security and privacy. We present an adaptive pruning scheme for edge devices in an FL system, which applies dataset-aware dynamic pruning for inference acceleration on Non-IID datasets.
• Score: 3.8666113275834335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning~(FL) has emerged as a new paradigm of training machine learning models without sacrificing data security and privacy. Learning models at edge devices such as cell phones is one of the most common use case of FL. However, the limited computing power and energy constraints of edge devices hinder the adoption of FL for both model training and deployment, especially for the resource-hungry Deep Neural Networks~(DNNs). To this end, many model compression methods have been proposed and network pruning is among the most well-known. However, a pruning policy for a given model is highly dataset-dependent, which is not suitable for non-Independent and Identically Distributed~(Non-IID) FL edge devices. In this paper, we present an adaptive pruning scheme for edge devices in an FL system, which applies dataset-aware dynamic pruning for inference acceleration on Non-IID datasets. Our evaluation shows that the proposed method accelerates inference by $2\times$~($50\%$ FLOPs reduction) while maintaining the model's quality on edge devices.

Related papers

• OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning [29.798780069556074]
  Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices. We propose OnDev-LCT: Lightweight Convolutional Transformers for On-Device vision tasks with limited training data and resources.
  arXiv  Detail & Related papers  (2024-01-22T02:17:36Z)
• Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks [72.59891661768177]
  Federated learning (FL) enables distributed learning across edge devices while protecting data privacy. We consider a FL framework with partial model pruning and personalization to overcome these challenges. This framework splits the learning model into a global part with model pruning shared with all devices to learn data representations and a personalized part to be fine-tuned for a specific device.
  arXiv  Detail & Related papers  (2023-09-04T21:10:45Z)
• NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients [44.89061671579694]
  Federated learning (FL) enables distributed training while preserving data privacy, but stragglers-slow or incapable clients-can significantly slow down the total training time and degrade performance. We propose nested federated learning (NeFL), a framework that efficiently divides deep neural networks into submodels using both depthwise and widthwise scaling. NeFL achieves performance gain, especially for the worst-case submodel compared to baseline approaches.
  arXiv  Detail & Related papers  (2023-08-15T13:29:14Z)
• Adaptive Federated Pruning in Hierarchical Wireless Networks [69.6417645730093]
  Federated Learning (FL) is a privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. In this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50 percent communication cost.
  arXiv  Detail & Related papers  (2023-05-15T22:04:49Z)
• Enhancing Efficiency in Multidevice Federated Learning through Data Selection [11.67484476827617]
  Federated learning (FL) in multidevice environments creates new opportunities to learn from a vast and diverse amount of private data. In this paper, we develop an FL framework to incorporate on-device data selection on such constrained devices. We show that our framework achieves 19% higher accuracy and 58% lower latency; compared to the baseline FL without our implemented strategies.
  arXiv  Detail & Related papers  (2022-11-08T11:39:17Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks [56.68149211499535]
  Federated learning (FL) is a key enabler for efficient communication and computing leveraging devices' distributed computing capabilities. This paper proposes a novel learning framework by integrating FL and width-adjustable slimmable neural networks (SNNs) We propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models.
  arXiv  Detail & Related papers  (2022-03-26T15:06:13Z)
• Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks [52.93232352968347]
  This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) FL preserves data privacy by exchanging the locally trained models of mobile devices. SNNs are however non-trivial, particularly under wireless connections with time-varying channel conditions. We propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models.
  arXiv  Detail & Related papers  (2021-12-05T11:17:17Z)
• Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices [40.69663094185572]
  Federated learning (FL) is a popular framework for training an AI model using distributed mobile data in a wireless network. One main challenge confronting practical FL is that resource constrained devices struggle with the computation intensive task of updating a deep-neural network model. To tackle the challenge, in this paper, a federated dropout (FedDrop) scheme is proposed building on the classic dropout scheme for random model pruning.
  arXiv  Detail & Related papers  (2021-09-30T16:52:13Z)
• 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.