Jan 29, 2020 · It maintains a small set of benchmark samples on the FL server and quantifies the credibility of the client local data without directly ...
Dealing with Label Quality Disparity in Federated Learning - SpringerLink
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Nov 26, 2020 · It is designed to identify participants with noisy labels and aggregate their model parameters into the FL model in an opportunistic manner. FOC ...
Federated Learning (FL) is highly useful for the applications which suffer silo effect and privacy preserving, such as healthcare, finance, education, etc.
FOCUS has been experimentally evaluated on both synthetic data and real-world data. The results show that it effectively identifies clients with noisy labels ...
Nov 26, 2020 · In this chapter, we propose an alternative approach to address this challenge. ... Then, a credit-weighted orchestration is performed to adjust ...
People also ask
What are the three types of federated learning?
Types of federated learning
Centralized federated learning. Centralized federated learning requires a central server. ...
Decentralized federated learning. Decentralized federated learning does not require a central server to coordinate the learning. ...
Heterogeneous federated learning.
What are the best practices of federated learning?
Best Practices for Success
Integrate privacy considerations into every step of the federated learning process. This involves using techniques such as differential privacy, secure multiparty computation, and encryption methods to protect data during all model training and aggregation stages.
What do you mean by federated learning?
Federated learning (also known as collaborative learning) is a sub-field of machine learning focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized.
What is the methodology of federated learning?
However, in a federated learning system, the learning methods are distributed across the edge devices themselves. Instead of centralizing the training data, only the model parameters are sent to individual devices like smartphones, where the learning process takes place locally on each device's data.
Dealing with Label Quality Disparity in Federated Learning
www.springerprofessional.de › dealing-w...
In this chapter, we propose an alternative approach to address this challenge. ... Then, a credit-weighted orchestration is performed to adjust the weight ...
Jan 29, 2020 · The results show that FOCUS effectively identifies clients with noisy labels and reduces their impact on the model performance, ...
Jun 21, 2022 · In federated learning, generating intermediate parameters for measuring label noise can relieve this issue. FOCUS [7] reduces the weight of low ...
Feb 1, 2023 · In this paper, we consider a federated learning setting with heterogeneous label noise, where each local client might observe training labels ...
In the federated setting, label noise is influenced by discrepancies in clients' labeling systems or the expertise of their users. This leads to varying label ...