Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach

• URL: http://arxiv.org/abs/2001.03836v4
• Date: Sat, 28 Mar 2020 15:20:20 GMT
• Title: Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach
• Authors: Xin Zhang, Minghong Fang, Jia Liu, and Zhengyuan Zhu
• Abstract summary: We propose a new decentralized gradient method for distributed edge learning. We show that SDM-DSGD improves the fundamental training-privacy trade-off by em two orders of magnitude.
• Score: 11.876314605344405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With rise of machine learning (ML) and the proliferation of smart mobile devices, recent years have witnessed a surge of interest in performing ML in wireless edge networks. In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing. Toward this end, we propose a new decentralized stochastic gradient method with sparse differential Gaussian-masked stochastic gradients (SDM-DSGD) for non-convex distributed edge learning. Our main contributions are three-fold: i) We theoretically establish the privacy and communication efficiency performance guarantee of our SDM-DSGD method, which outperforms all existing works; ii) We show that SDM-DSGD improves the fundamental training-privacy trade-off by {\em two orders of magnitude} compared with the state-of-the-art. iii) We reveal theoretical insights and offer practical design guidelines for the interactions between privacy preservation and communication efficiency, two conflicting performance goals. We conduct extensive experiments with a variety of learning models on MNIST and CIFAR-10 datasets to verify our theoretical findings. Collectively, our results contribute to the theory and algorithm design for distributed edge learning.

Related papers

• Multi-Agent Reinforcement Learning from Human Feedback: Data Coverage and Algorithmic Techniques [65.55451717632317]
  We study Multi-Agent Reinforcement Learning from Human Feedback (MARLHF), exploring both theoretical foundations and empirical validations. We define the task as identifying Nash equilibrium from a preference-only offline dataset in general-sum games. Our findings underscore the multifaceted approach required for MARLHF, paving the way for effective preference-based multi-agent systems.
  arXiv  Detail & Related papers  (2024-09-01T13:14:41Z)
• Distributed Learning for Wi-Fi AP Load Prediction [1.2057886807886689]
  We study the application of the two cornerstones of distributed learning, namely Federated Learning (FL) and Knowledge Distillation (KD) We prove that distributed learning can improve the predictive accuracy centralized ML solutions by up to 93% while reducing the communication overheads and the energy cost by 80%.
  arXiv  Detail & Related papers  (2024-04-22T16:37:35Z)
• An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms [2.994794762377111]
  In today's world, the rapid expansion of IoT networks and the proliferation of smart devices have resulted in the generation of substantial amounts of heterogeneous data. To handle this data effectively, advanced data processing technologies are necessary to guarantee the preservation of both privacy and efficiency. Federated learning emerged as a distributed learning method that trains models locally and aggregates them on a server to preserve data privacy.
  arXiv  Detail & Related papers  (2023-12-24T00:13:41Z)
• Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation [6.738522094694818]
  Semi-supervised medical image segmentation (SSMIS) has witnessed substantial advancements by leveraging limited labeled data and abundant unlabeled data. Existing state-of-the-art (SOTA) methods encounter challenges in accurately predicting labels for the unlabeled data. We propose a novel Cross-head mutual mean-teaching Network (CMMT-Net) incorporated strong-weak data augmentation.
  arXiv  Detail & Related papers  (2023-10-08T09:13:04Z)
• Decentralized Learning over Wireless Networks: The Effect of Broadcast with Random Access [56.91063444859008]
  We investigate the impact of broadcast transmission and probabilistic random access policy on the convergence performance of D-SGD. Our results demonstrate that optimizing the access probability to maximize the expected number of successful links is a highly effective strategy for accelerating the system convergence.
  arXiv  Detail & Related papers  (2023-05-12T10:32:26Z)
• Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity [6.341508488542275]
  Federated edge learning (FEEL) has attracted much attention as a privacy-preserving paradigm to effectively incorporate the distributed data at the network edge for training deep learning models. In this paper, we investigate a novel framework of FEEL, namely semi-decentralized federated edge learning (SD-FEEL), where multiple edge servers are employed to collectively coordinate a large number of client nodes. By exploiting the low-latency communication among edge servers for efficient model sharing, SD-FEEL can incorporate more training data, while enjoying much lower latency compared with conventional federated learning.
  arXiv  Detail & Related papers  (2021-12-20T03:06:08Z)
• Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data [14.269800282001464]
  Federated edge learning (FEEL) has emerged as an effective approach to reduce the large communication latency in Cloud-based machine learning solutions. We investigate a novel framework of FEEL, namely semi-decentralized federated edge learning (SD-FEEL) By allowing model aggregation across different edge clusters, SD-FEEL enjoys the benefit of FEEL in reducing the training latency.
  arXiv  Detail & Related papers  (2021-04-26T16:11:47Z)
• Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement [56.40587594647692]
  We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED) TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
  arXiv  Detail & Related papers  (2020-10-16T17:45:08Z)
• Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles [52.79089414630366]
  We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL) We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
  arXiv  Detail & Related papers  (2020-07-27T12:38:37Z)
• A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning [72.30054522048553]
  We present a new method, Transductive Multi-Head Few-Shot learning (TMHFS), to address the Cross-Domain Few-Shot Learning challenge. The proposed methods greatly outperform the strong baseline, fine-tuning, on four different target domains.
  arXiv  Detail & Related papers  (2020-06-08T02:39:59Z)
• Unpaired Multi-modal Segmentation via Knowledge Distillation [77.39798870702174]
  We propose a novel learning scheme for unpaired cross-modality image segmentation. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI. We have extensively validated our approach on two multi-class segmentation problems.
  arXiv  Detail & Related papers  (2020-01-06T20:03:17Z)

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.