Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space

• URL: http://arxiv.org/abs/2312.09817v2
• Date: Tue, 9 Jan 2024 20:02:12 GMT
• Title: Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space
• Authors: Mohsin Hasan, Guojun Zhang, Kaiyang Guo, Xi Chen, Pascal Poupart
• Abstract summary: Federated Learning (FL) involves training a model over a dataset distributed among clients. Small and noisy datasets are common, highlighting the need for well-calibrated models. We propose $beta$-Predictive Bayes, a Bayesian FL algorithm that interpolates between a mixture and product of the predictive posteriors.
• Score: 27.259110269667826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) involves training a model over a dataset distributed among clients, with the constraint that each client's dataset is localized and possibly heterogeneous. In FL, small and noisy datasets are common, highlighting the need for well-calibrated models that represent the uncertainty of predictions. The closest FL techniques to achieving such goals are the Bayesian FL methods which collect parameter samples from local posteriors, and aggregate them to approximate the global posterior. To improve scalability for larger models, one common Bayesian approach is to approximate the global predictive posterior by multiplying local predictive posteriors. In this work, we demonstrate that this method gives systematically overconfident predictions, and we remedy this by proposing $\beta$-Predictive Bayes, a Bayesian FL algorithm that interpolates between a mixture and product of the predictive posteriors, using a tunable parameter $\beta$. This parameter is tuned to improve the global ensemble's calibration, before it is distilled to a single model. Our method is evaluated on a variety of regression and classification datasets to demonstrate its superiority in calibration to other baselines, even as data heterogeneity increases. Code available at https://github.com/hasanmohsin/betaPredBayesFL

Related papers

• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• FedHB: Hierarchical Bayesian Federated Learning [11.936836827864095]
  We propose a novel hierarchical Bayesian approach to Federated Learning (FL) Our model reasonably describes the generative process of clients' local data via hierarchical Bayesian modeling. We show that our block-coordinate FL algorithm converges to an optimum of the objective at the rate of $O(sqrtt)$.
  arXiv  Detail & Related papers  (2023-05-08T18:21:41Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering [6.829317124629158]
  Federated learning (FL) is a promising framework that models distributed machine learning. FL suffers performance degradation from heterogeneous and limited data. We present a novel personalized Bayesian FL approach named pFedBayes and a clustered FL model named cFedbayes.
  arXiv  Detail & Related papers  (2023-03-08T02:52:40Z)
• Federated Learning with Label Distribution Skew via Logits Calibration [26.98248192651355]
  In this paper, we investigate the label distribution skew in FL, where the distribution of labels varies across clients. We propose FedLC, which calibrates the logits before softmax cross-entropy according to the probability of occurrence of each class. Experiments on federated datasets and real-world datasets demonstrate that FedLC leads to a more accurate global model.
  arXiv  Detail & Related papers  (2022-09-01T02:56:39Z)
• Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks [76.16527095195893]
  We investigate the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs) In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based regressor to model the content request pattern. We utilize Bayesian learning to train the model parameters, which is robust to overfitting.
  arXiv  Detail & Related papers  (2022-06-23T03:05:12Z)
• $\texttt{FedBC}$: Calibrating Global and Local Models via Federated Learning Beyond Consensus [66.62731854746856]
  In federated learning (FL), the objective of collaboratively learning a global model through aggregation of model updates across devices tends to oppose the goal of personalization via local information. In this work, we calibrate this tradeoff in a quantitative manner through a multi-criterion-based optimization. We demonstrate that $texttFedBC$ balances the global and local model test accuracy metrics across a suite datasets.
  arXiv  Detail & Related papers  (2022-06-22T02:42:04Z)
• Robust One Round Federated Learning with Predictive Space Bayesian Inference [19.533268415744338]
  We show how the global predictive posterior can be approximated using client predictive posteriors. We present an algorithm based on this idea, which performs MCMC sampling at each client to obtain an estimate of the local posterior, and then aggregates these in one round to obtain a global ensemble model.
  arXiv  Detail & Related papers  (2022-06-20T01:06:59Z)
• Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning [5.882234707363695]
  Fed-ensemble brings model ensembling to federated learning (FL) Fed-ensemble can be readily utilized within established FL methods.
  arXiv  Detail & Related papers  (2021-07-21T14:40:14Z)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)
• Model Fusion with Kullback--Leibler Divergence [58.20269014662046]
  We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors.
  arXiv  Detail & Related papers  (2020-07-13T03:27:45Z)

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.