Federated Learning with Uncertainty via Distilled Predictive Distributions

• URL: http://arxiv.org/abs/2206.07562v2
• Date: Sun, 1 Oct 2023 16:18:46 GMT
• Title: Federated Learning with Uncertainty via Distilled Predictive Distributions
• Authors: Shrey Bhatt, Aishwarya Gupta, Piyush Rai
• Abstract summary: We present a framework for federated learning with uncertainty where, in each round, each client infers the posterior distribution over its parameters as well as the posterior predictive distribution (PPD) Unlike some of the recent Bayesian approaches to federated learning, our approach does not require sending the whole posterior distribution of the parameters from each client to the server. Our approach does not make any restrictive assumptions, such as the form of the clients' posterior distributions, or of their PPDs.
• Score: 14.828509220023387
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Most existing federated learning methods are unable to estimate model/predictive uncertainty since the client models are trained using the standard loss function minimization approach which ignores such uncertainties. In many situations, however, especially in limited data settings, it is beneficial to take into account the uncertainty in the model parameters at each client as it leads to more accurate predictions and also because reliable estimates of uncertainty can be used for tasks, such as out-of-distribution (OOD) detection, and sequential decision-making tasks, such as active learning. We present a framework for federated learning with uncertainty where, in each round, each client infers the posterior distribution over its parameters as well as the posterior predictive distribution (PPD), distills the PPD into a single deep neural network, and sends this network to the server. Unlike some of the recent Bayesian approaches to federated learning, our approach does not require sending the whole posterior distribution of the parameters from each client to the server but only the PPD in the distilled form as a deep neural network. In addition, when making predictions at test time, it does not require computationally expensive Monte-Carlo averaging over the posterior distribution because our approach always maintains the PPD in the form of a single deep neural network. Moreover, our approach does not make any restrictive assumptions, such as the form of the clients' posterior distributions, or of their PPDs. We evaluate our approach on classification in federated setting, as well as active learning and OOD detection in federated settings, on which our approach outperforms various existing federated learning baselines.

Related papers

• Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune [3.1001287855313966]
  We introduce a novel framework for personalized federated learning, incorporating Bayesian methodology. We show that the new algorithm not only improves accuracy but also outperforms the baseline significantly in OOD detection.
  arXiv  Detail & Related papers  (2024-02-25T13:28:08Z)
• Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation [11.66240919177989]
  We propose a new method, Generative Posterior Networks (GPNs), that uses unlabeled data to estimate uncertainty in high-dimensional problems. A GPN is a generative model that, given a prior distribution over functions, approximates the posterior distribution directly by regularizing the network towards samples from the prior.
  arXiv  Detail & Related papers  (2023-12-29T00:43:41Z)
• Tractable Function-Space Variational Inference in Bayesian Neural Networks [72.97620734290139]
  A popular approach for estimating the predictive uncertainty of neural networks is to define a prior distribution over the network parameters. We propose a scalable function-space variational inference method that allows incorporating prior information. We show that the proposed method leads to state-of-the-art uncertainty estimation and predictive performance on a range of prediction tasks.
  arXiv  Detail & Related papers  (2023-12-28T18:33:26Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Training on Test Data with Bayesian Adaptation for Covariate Shift [96.3250517412545]
  Deep neural networks often make inaccurate predictions with unreliable uncertainty estimates. We derive a Bayesian model that provides for a well-defined relationship between unlabeled inputs under distributional shift and model parameters. We show that our method improves both accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2021-09-27T01:09:08Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts [33.45069308137142]
  Posterior Network (PostNet) predicts an individual closed-form posterior distribution over predicted probabilites for any input sample. PostNet achieves state-of-the art results in OOD detection and in uncertainty calibration under dataset shifts.
  arXiv  Detail & Related papers  (2020-06-16T15:16:32Z)
• Uncertainty-Aware Deep Classifiers using Generative Models [7.486679152591502]
  Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. We develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions.
  arXiv  Detail & Related papers  (2020-06-07T15:38:35Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

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.