Multi-task Over-the-Air Federated Learning: A Non-Orthogonal Transmission Approach

• URL: http://arxiv.org/abs/2106.14229v2
• Date: Tue, 29 Jun 2021 07:34:10 GMT
• Title: Multi-task Over-the-Air Federated Learning: A Non-Orthogonal Transmission Approach
• Authors: Haoming Ma, Xiaojun Yuan, Dian Fan, Zhi Ding, Xin Wang
• Abstract summary: We propose a multi-task over-theair federated learning (MOAFL) framework, where multiple learning tasks share edge devices for data collection and learning models under the coordination of a edge server (ES) Both the convergence analysis and numerical results demonstrate that the MOAFL framework can significantly reduce the uplink bandwidth consumption of multiple tasks without causing substantial learning performance degradation.
• Score: 52.85647632037537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this letter, we propose a multi-task over-theair federated learning (MOAFL) framework, where multiple learning tasks share edge devices for data collection and learning models under the coordination of a edge server (ES). Specially, the model updates for all the tasks are transmitted and superpositioned concurrently over a non-orthogonal uplink channel via over-the-air computation, and the aggregation results of all the tasks are reconstructed at the ES through an extended version of the turbo compressed sensing algorithm. Both the convergence analysis and numerical results demonstrate that the MOAFL framework can significantly reduce the uplink bandwidth consumption of multiple tasks without causing substantial learning performance degradation.

Related papers

• Learning Representation for Multitask learning through Self Supervised Auxiliary learning [3.236198583140341]
  In the hard parameter sharing approach, an encoder shared through multiple tasks generates data representations passed to task-specific predictors. We propose Dummy Gradient norm Regularization that aims to improve the universality of the representations generated by the shared encoder. We show that DGR effectively improves the quality of the shared representations, leading to better multi-task prediction performances.
  arXiv  Detail & Related papers  (2024-09-25T06:08:35Z)
• A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset [44.94304541427113]
  We propose a multitask deep learning model to perform multiple classification and regression tasks simultaneously on hyperspectral images. We validated our approach on a large hyperspectral dataset called TAIGA. A comprehensive qualitative and quantitative analysis of the results shows that the proposed method significantly outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-23T11:14:54Z)
• Over-the-Air Federated Multi-Task Learning via Model Sparsification and Turbo Compressed Sensing [48.19771515107681]
  We propose an over-the-air FMTL framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server. In OA-FMTL, the local updates of edge devices are sparsified, compressed, and then sent over the uplink channel in a superimposed fashion. We analyze the performance of the proposed OA-FMTL framework together with the M-Turbo-CS algorithm.
  arXiv  Detail & Related papers  (2022-05-08T08:03:52Z)
• Semi-supervised Multi-task Learning for Semantics and Depth [88.77716991603252]
  Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance. We propose the Semi-supervised Multi-Task Learning (MTL) method to leverage the available supervisory signals from different datasets. We present a domain-aware discriminator structure with various alignment formulations to mitigate the domain discrepancy issue among datasets.
  arXiv  Detail & Related papers  (2021-10-14T07:43:39Z)
• Multi-Task Learning with Sequence-Conditioned Transporter Networks [67.57293592529517]
  We aim to solve multi-task learning through the lens of sequence-conditioning and weighted sampling. We propose a new suite of benchmark aimed at compositional tasks, MultiRavens, which allows defining custom task combinations. Second, we propose a vision-based end-to-end system architecture, Sequence-Conditioned Transporter Networks, which augments Goal-Conditioned Transporter Networks with sequence-conditioning and weighted sampling.
  arXiv  Detail & Related papers  (2021-09-15T21:19:11Z)
• HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks [4.095907708855597]
  Multi-task learning (MTL) can improve performance on a task by sharing representations with one or more related auxiliary-tasks. Usually, MTL-networks are trained on a composite loss function formed by a constant weighted combination of the separate task losses. In practice, constant loss weights lead to poor results for two reasons: (i) for mini-batch based optimisation, the optimal task weights vary significantly from one update to the next depending on mini-batch sample composition. We introduce HydaLearn, an intelligent weighting algorithm that connects main-task gain to the individual task gradients, in order to inform
  arXiv  Detail & Related papers  (2020-08-26T16:04:02Z)
• Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference [75.95287293847697]
  Two common challenges in developing multi-task models are often overlooked in literature. First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning) Second, eliminating adverse interactions amongst tasks, which has been shown to significantly degrade the single-task performance in a multi-task setup (task interference)
  arXiv  Detail & Related papers  (2020-07-24T14:44:46Z)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)

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.