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FedFixer: A Machine Learning Algorithm with the Dual Model Structure to Mitigate the Impact of Heterogeneous Noisy Label Samples in Federated Learning
In today’s world, where data is distributed across various locations and privacy is paramount, Federated Learning (FL) has emerged as a game-changing solution. It enables multiple parties to train machine learning models collaboratively without sharing their data, ensuring that sensitive information remains locally stored and protected.
Challenges in Federated Learning
A significant challenge arises when the data labels provided by human annotators are imperfect, leading to heterogeneous label noise distributions across different parties involved in the federated learning process. This issue can severely undermine the performance of FL models, hindering their ability to generalize effectively and make accurate predictions.
Introducing FedFixer
To tackle this issue head-on, a team of researchers has proposed FedFixer, an innovative algorithm that leverages a dual model structure consisting of a global model and a personalized model. The global model benefits from aggregated updates across clients, while the personalized model is specifically designed to adapt to the unique characteristics of each client’s data, including client-specific samples and label noise patterns.
Key Regularization Techniques
FedFixer incorporates two key regularization techniques to combat potential overfitting of the dual models. The confidence regularizer modifies the traditional Cross-Entropy loss function to encourage confident predictions and reduce the influence of noisy label samples. The distance regularizer constrains the disparity between the personalized and global models, preventing overfitting to local noisy data due to limited sample size.
Practical Applications
The effectiveness of FedFixer has been extensively validated through experiments on benchmark datasets, demonstrating its potential for real-world applications, such as in the healthcare domain, where accurate and reliable predictions are crucial.
AI Solutions for Your Company
If you want to evolve your company with AI, stay competitive, and use FedFixer to mitigate the impact of heterogeneous noisy label samples in Federated Learning. Identify automation opportunities, define KPIs, select an AI solution, and implement gradually to leverage AI effectively.
Practical AI Solution: AI Sales Bot
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For AI KPI management advice, connect with us at hello@itinai.com. For continuous insights into leveraging AI, stay tuned on our Telegram channel or Twitter.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- FedFixer: A Machine Learning Algorithm with the Dual Model Structure to Mitigate the Impact of Heterogeneous Noisy Label Samples in Federated Learning
- MarkTechPost
- Twitter – @itinaicom
