Summary of Flame: Adaptive and Reactive Concept Drift Mitigation For Federated Learning Deployments, by Ioannis Mavromatis and Stefano De Feo and Aftab Khan
FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments
by Ioannis Mavromatis, Stefano De Feo, Aftab Khan
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce Federated Learning with Adaptive Monitoring and Elimination (FLAME), a novel solution designed to detect and mitigate concept drift in Federated Learning (FL) Internet of Things (IoT) environments. The authors develop an FL architecture that considers real-world FL pipelines and demonstrates its ability to maintain model performance while addressing bandwidth and privacy constraints. By leveraging various features and extensions from previous works, FLAME offers a robust solution to concept drift, reducing computational load and communication overhead. Compared to lightweight mitigation methods, FLAME shows superior performance in maintaining high F1 scores and reducing resource utilisation in large-scale IoT deployments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way for devices like smart home appliances or vehicles to learn from each other without sharing their own data. This paper helps make it work better by detecting when the rules change and adjusting accordingly. The new approach, called FLAME, makes sure the learning model stays accurate and efficient even in changing environments. It’s important because this kind of learning is used in many real-world applications, like controlling traffic lights or monitoring air quality. |
Keywords
» Artificial intelligence » Federated learning
