Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels

• URL: http://arxiv.org/abs/2208.09378v3
• Date: Fri, 26 May 2023 14:08:35 GMT
• Title: Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels
• Authors: Vasileios Tsouvalas, Aaqib Saeed, Tanir Ozcelebi, Nirvana Meratnia
• Abstract summary: 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%.
• Score: 3.4620497416430456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets, where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are readily available on users' devices; in reality, label noise can naturally occur in FL and is closely related to clients' characteristics. Due to scarcity of available data and significant label noise variations among clients in FL, existing state-of-the-art centralized approaches exhibit unsatisfactory performance, while prior FL studies rely on excessive on-device computational schemes or additional clean data available on server. Here, we propose FedLN, a framework to deal with label noise across different FL training stages; namely, FL initialization, on-device model training, and server model aggregation, able to accommodate the diverse computational capabilities of devices in a FL system. Specifically, FedLN computes per-client noise-level estimation in a single federated round and improves the models' performance by either correcting or mitigating the effect of noisy samples. 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%. We further validate the efficiency of FedLN in human-annotated real-world noisy datasets and report a 4.8% increase on average in models' recognition performance, highlighting that~\method~can be useful for improving FL services provided to everyday users.

Related papers

• 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)
• 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)
• Quantifying the Impact of Label Noise on Federated Learning [7.531486350989069]
  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.
  arXiv  Detail & Related papers  (2022-11-15T00:40:55Z)
• 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)
• Over-the-Air Federated Learning from Heterogeneous Data [107.05618009955094]
  Federated learning (FL) is a framework for distributed learning of centralized models. We develop a Convergent OTA FL (COTAF) algorithm which enhances the common local gradient descent (SGD) FL algorithm. We numerically show that the precoding induced by COTAF notably improves the convergence rate and the accuracy of models trained via OTA FL.
  arXiv  Detail & Related papers  (2020-09-27T08:28:25Z)
• FOCUS: Dealing with Label Quality Disparity in Federated Learning [25.650278226178298]
  We propose Federated Opportunistic Computing for Ubiquitous Systems (FOCUS) to address this challenge. FOCUS quantifies the credibility of the client local data without directly observing them. It effectively identifies clients with noisy labels and reduces their impact on the model performance.
  arXiv  Detail & Related papers  (2020-01-29T09:31:01Z)

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.