Summary of Safeloc: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning For Indoor Localization, by Akhil Singampalli et al.
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
by Akhil Singampalli, Danish Gufran, Sudeep Pasricha
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 The proposed SAFELOC framework addresses the limitations of conventional machine learning (ML) based indoor localization solutions by introducing a novel distributed and cooperative learning environment that utilizes federated learning (FL) to preserve user data privacy. The framework minimizes localization errors under challenging conditions caused by device heterogeneity and ML data poisoning attacks, while ensuring model compactness for efficient mobile device deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAFELOC is a new approach that helps phones find their location indoors despite differences in devices and possible cyberattacks. It uses special networks to detect when someone might be trying to trick the system, and then adapts to make sure the phone gets accurate directions. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
