Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning

• URL: http://arxiv.org/abs/2003.09603v2
• Date: Mon, 20 Sep 2021 18:56:41 GMT
• Title: Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning
• Authors: Shaoxiong Ji and Wenqi Jiang and Anwar Walid and Xue Li
• Abstract summary: Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization. This paper introduces two approaches for improving communication efficiency by dynamic sampling and top-$k$ selective masking.
• Score: 11.511755449420253
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization. Deep neural networks' rapid development facilitates the learning techniques for modeling complex problems and emerges into federated deep learning under the federated setting. However, the tremendous amount of model parameters burdens the communication network with a high load of transportation. This paper introduces two approaches for improving communication efficiency by dynamic sampling and top-$k$ selective masking. The former controls the fraction of selected client models dynamically, while the latter selects parameters with top-$k$ largest values of difference for federated updating. Experiments on convolutional image classification and recurrent language modeling are conducted on three public datasets to show our proposed methods' effectiveness.

Related papers

• FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning [6.648544684097181]
  multimodal models integrate vision and language into visual-language models (VLMs) This paper proposes a novel approach that leverages Federated Learning and parameter-efficient adapters to train VLMs. Our approach accelerates training time by up to 34.72 times and requires 2.47 times less memory usage than full fine-tuning.
  arXiv  Detail & Related papers  (2024-04-12T00:36:43Z)
• Transfer Learning with Reconstruction Loss [12.906500431427716]
  This paper proposes a novel approach for model training by adding into the model an additional reconstruction stage associated with a new reconstruction loss. The proposed approach encourages the learned features to be general and transferable, and therefore can be readily used for efficient transfer learning. For numerical simulations, three applications are studied: transfer learning on classifying MNIST handwritten digits, the device-to-device wireless network power allocation, and the multiple-input-single-output network downlink beamforming and localization.
  arXiv  Detail & Related papers  (2024-03-31T00:22:36Z)
• Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters [65.15700861265432]
  We present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models. Our approach involves the dynamic expansion of a pre-trained CLIP model, through the integration of Mixture-of-Experts (MoE) adapters. To preserve the zero-shot recognition capability of vision-language models, we introduce a Distribution Discriminative Auto-Selector.
  arXiv  Detail & Related papers  (2024-03-18T08:00:23Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine [0.9576975587953563]
  Pre-trained language models (PLM) based on transformer neural networks offer great opportunities to improve automatic content analysis in communication science. Three characteristics so far impeded the widespread adoption of the methods in the applying disciplines: the dominance of English language models in NLP research, the necessary computing resources, and the effort required to produce training data to fine-tune PLMs. We test our approach on a realistic use case from communication science to automatically detect claims and arguments together with their stance in the German news debate on arms deliveries to Ukraine.
  arXiv  Detail & Related papers  (2023-12-28T11:39:08Z)
• Personalized Federated Learning with Contextual Modulation and Meta-Learning [2.7716102039510564]
  Federated learning has emerged as a promising approach for training machine learning models on decentralized data sources. We propose a novel framework that combines federated learning with meta-learning techniques to enhance both efficiency and generalization capabilities.
  arXiv  Detail & Related papers  (2023-12-23T08:18:22Z)
• FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data [54.81695390763957]
  Federated learning is an emerging distributed machine learning method. We propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate. We show that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients.
  arXiv  Detail & Related papers  (2023-09-18T12:35:05Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• An Expectation-Maximization Perspective on Federated Learning [75.67515842938299]
  Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated federated learning process as a hierarchical latent variable model where the server provides the parameters of a prior distribution over the client-specific model parameters. We show that with simple Gaussian priors and a hard version of the well known Expectation-Maximization (EM) algorithm, learning in such a model corresponds to FedAvg, the most popular algorithm for the federated learning setting.
  arXiv  Detail & Related papers  (2021-11-19T12:58:59Z)
• Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks [82.33619654835348]
  Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity. In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
  arXiv  Detail & Related papers  (2020-08-29T08:39:43Z)
• Real-time Federated Evolutionary Neural Architecture Search [14.099753950531456]
  Federated learning is a distributed machine learning approach to privacy preservation. We propose an evolutionary approach to real-time federated neural architecture search that not only optimize the model performance but also reduces the local payload. This way, we effectively reduce computational and communication costs required for evolutionary optimization and avoid big performance fluctuations of the local models.
  arXiv  Detail & Related papers  (2020-03-04T17:03:28Z)

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.