FOCUS: Dealing with Label Quality Disparity in Federated Learning
- URL: http://arxiv.org/abs/2001.11359v1
- Date: Wed, 29 Jan 2020 09:31:01 GMT
- Title: FOCUS: Dealing with Label Quality Disparity in Federated Learning
- Authors: Yiqiang Chen, Xiaodong Yang, Xin Qin, Han Yu, Biao Chen, Zhiqi Shen
- Abstract summary: 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.
- Score: 25.650278226178298
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Ubiquitous systems with End-Edge-Cloud architecture are increasingly being
used in healthcare applications. Federated Learning (FL) is highly useful for
such applications, due to silo effect and privacy preserving. Existing FL
approaches generally do not account for disparities in the quality of local
data labels. However, the clients in ubiquitous systems tend to suffer from
label noise due to varying skill-levels, biases or malicious tampering of the
annotators. In this paper, we propose Federated Opportunistic Computing for
Ubiquitous Systems (FOCUS) to address this challenge. It maintains a small set
of benchmark samples on the FL server and quantifies the credibility of the
client local data without directly observing them by computing the mutual
cross-entropy between performance of the FL model on the local datasets and
that of the client local FL model on the benchmark dataset. Then, a credit
weighted orchestration is performed to adjust the weight assigned to clients in
the FL model based on their credibility values. FOCUS has been experimentally
evaluated on both synthetic data and real-world data. The results show that it
effectively identifies clients with noisy labels and reduces their impact on
the model performance, thereby significantly outperforming existing FL
approaches.
Related papers
- FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation [15.298650496155508]
Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server.
Existing FL methods face challenges when dealing with scarce and label-skewed data across devices, resulting in local model overfitting and drift.
We propose a pioneering framework called FLea, incorporating the following key components.
arXiv Detail & Related papers (2024-06-13T19:28:08Z) - 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) - FLASH: Federated Learning Across Simultaneous Heterogeneities [54.80435317208111]
FLASH(Federated Learning Across Simultaneous Heterogeneities) is a lightweight and flexible client selection algorithm.
It outperforms state-of-the-art FL frameworks under extensive sources of Heterogeneities.
It achieves substantial and consistent improvements over state-of-the-art baselines.
arXiv Detail & Related papers (2024-02-13T20:04:39Z) - PFL-GAN: When Client Heterogeneity Meets Generative Models in
Personalized Federated Learning [55.930403371398114]
We propose a novel generative adversarial network (GAN) sharing and aggregation strategy for personalized learning (PFL)
PFL-GAN addresses the client heterogeneity in different scenarios. More specially, we first learn the similarity among clients and then develop an weighted collaborative data aggregation.
The empirical results through the rigorous experimentation on several well-known datasets demonstrate the effectiveness of PFL-GAN.
arXiv Detail & Related papers (2023-08-23T22:38:35Z) - Rethinking Client Drift in Federated Learning: A Logit Perspective [125.35844582366441]
Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection.
We find that the difference in logits between the local and global models increases as the model is continuously updated.
We propose a new algorithm, named FedCSD, a Class prototype Similarity Distillation in a federated framework to align the local and global models.
arXiv Detail & Related papers (2023-08-20T04:41:01Z) - FL Games: A Federated Learning Framework for Distribution Shifts [71.98708418753786]
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server.
We propose FL GAMES, a game-theoretic framework for federated learning that learns causal features that are invariant across clients.
arXiv Detail & Related papers (2022-10-31T22:59:03Z) - 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) - Federated Learning in Non-IID Settings Aided by Differentially Private
Synthetic Data [20.757477553095637]
Federated learning (FL) is a privacy-promoting framework that enables clients to collaboratively train machine learning models.
A major challenge in federated learning arises when the local data is heterogeneous.
We propose FedDPMS, an FL algorithm in which clients deploy variational auto-encoders to augment local datasets with data synthesized using differentially private means of latent data representations.
arXiv Detail & Related papers (2022-06-01T18:00:48Z) - Federated Learning from Only Unlabeled Data with
Class-Conditional-Sharing Clients [98.22390453672499]
Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data.
We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients.
arXiv Detail & Related papers (2022-04-07T09:12:00Z) - Semi-Supervised Federated Learning with non-IID Data: Algorithm and
System Design [42.63120623012093]
Federated Learning (FL) allows edge devices (or clients) to keep data locally while simultaneously training a shared global model.
The distribution of the client's local training data is non-independent identically distributed (non-IID)
We present a robust semi-supervised FL system design, where the system aims to solve the problem of data availability and non-IID in FL.
arXiv Detail & Related papers (2021-10-26T03:41:48Z)
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.