Evaluating Adversarial Attacks on Federated Learning for Temperature Forecasting
Published:Dec 15, 2025 11:22
•1 min read
•ArXiv
Analysis
This article likely investigates the vulnerability of federated learning models used for temperature forecasting to adversarial attacks. It would analyze how these attacks can compromise the accuracy and reliability of the forecasting models. The research would likely involve designing and testing different attack strategies and evaluating their impact on the model's performance.
Key Takeaways
- •Focuses on the security of federated learning in a specific application (temperature forecasting).
- •Examines the impact of adversarial attacks on model accuracy.
- •Likely explores different attack strategies and their effectiveness.
Reference
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