Quantifying the Impact of Label Noise on Federated Learning

• URL: http://arxiv.org/abs/2211.07816v7
• Date: Mon, 3 Apr 2023 09:45:32 GMT
• Title: Quantifying the Impact of Label Noise on Federated Learning
• Authors: Shuqi Ke, Chao Huang, Xin Liu
• Abstract summary: Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets. This paper provides a quantitative study on the impact of label noise on FL. Our empirical results show that the global model accuracy linearly decreases as the noise level increases.
• Score: 7.531486350989069
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets. While existing studies focus on FL algorithm development to tackle data heterogeneity across clients, the important issue of data quality (e.g., label noise) in FL is overlooked. This paper aims to fill this gap by providing a quantitative study on the impact of label noise on FL. We derive an upper bound for the generalization error that is linear in the clients' label noise level. Then we conduct experiments on MNIST and CIFAR-10 datasets using various FL algorithms. Our empirical results show that the global model accuracy linearly decreases as the noise level increases, which is consistent with our theoretical analysis. We further find that label noise slows down the convergence of FL training, and the global model tends to overfit when the noise level is high.

Related papers

• Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning? [50.03434441234569]
  Federated Learning (FL) has gained significant popularity due to its effectiveness in training machine learning models across diverse sites without requiring direct data sharing. While various algorithms have shown that FL with local updates is a communication-efficient distributed learning framework, the generalization performance of FL with local updates has received comparatively less attention.
  arXiv  Detail & Related papers  (2024-09-05T19:00:18Z)
• Collaboratively Learning Federated Models from Noisy Decentralized Data [21.3209961590772]
  Federated learning (FL) has emerged as a prominent method for collaboratively training machine learning models using local data from edge devices. We focus on addressing the problem of noisy data in the input space, an under-explored area compared to the label noise. We propose a noise-aware FL aggregation method, namely Federated Noise-Sifting (FedNS), which can be used as a plug-in approach in conjunction with widely used FL strategies.
  arXiv  Detail & Related papers  (2024-09-03T18:00:51Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise [10.417576145123256]
  Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data. In this paper, we, for the first time, identify and tackle this problem. Experiments on two real-world medical image segmentation datasets demonstrate the superior performance of FedA$3$I against state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-12-20T08:42:57Z)
• FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels [99.70895640578816]
  Federated learning with noisy labels (F-LNL) aims at seeking an optimal server model via collaborative distributed learning. We present FedDiv to tackle the challenges of F-LNL. Specifically, we propose a global noise filter called Federated Noise Filter.
  arXiv  Detail & Related papers  (2023-12-19T15:46:47Z)
• Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients [4.782145666637457]
  Federated learning (FL) is a distributed framework for collaboratively training with privacy guarantees. In real-world scenarios, clients may have Non-IID data (local class imbalance) with poor annotation quality (label noise) We propose FedCNI without using an additional clean proxy dataset. It includes a noise-resilient local solver and a robust global aggregator.
  arXiv  Detail & Related papers  (2023-04-06T06:47:14Z)
• Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels [3.4620497416430456]
  Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets. We propose FedLN, a framework to deal with label noise across different FL training stages. Our evaluation on various publicly available vision and audio datasets demonstrate a 22% improvement on average compared to other existing methods for a label noise level of 60%.
  arXiv  Detail & Related papers  (2022-08-19T14:47:40Z)
• FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels [49.47228898303909]
  Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Local training on noisy labels can easily result in overfitting to noisy labels, which is devastating to the global model through aggregation. We develop a simple two-level sampling method "FedNoiL" that selects clients for more robust global aggregation on the server.
  arXiv  Detail & Related papers  (2022-05-20T12:06:39Z)
• FedCorr: Multi-Stage Federated Learning for Label Noise Correction [80.9366438220228]
  Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model. We propose $textttFedCorr$, a general multi-stage framework to tackle heterogeneous label noise in FL. Experiments conducted on CIFAR-10/100 with federated synthetic label noise, and on a real-world noisy dataset, Clothing1M, demonstrate that $textttFedCorr$ is robust to label noise.
  arXiv  Detail & Related papers  (2022-04-10T12:51:18Z)
• Federated Noisy Client Learning [105.00756772827066]
  Federated learning (FL) collaboratively aggregates a shared global model depending on multiple local clients. Standard FL methods ignore the noisy client issue, which may harm the overall performance of the aggregated model. We propose Federated Noisy Client Learning (Fed-NCL), which is a plug-and-play algorithm and contains two main components.
  arXiv  Detail & Related papers  (2021-06-24T11:09:17Z)

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.