Summary of Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-free Feature Recalibration, by Vasilis Siomos et al.
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration
by Vasilis Siomos, Sergio Naval-Marimont, Jonathan Passerat-Palmbach, Giacomo Tarroni
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 address the issue of statistical heterogeneity in federated learning, a collaborative training paradigm where data remains decentralized. The team proposes Adaptive Normalization-free Feature Recalibration (ANFR), an architecture-level approach that normalizes layer weights instead of activations and uses channel attention to suppress inconsistent features between clients. By combining these techniques, ANFR boosts model performance and enhances class selectivity. The method operates independently of the aggregation method and is effective in both global and personalized federated learning settings, with minimal computational overhead. Additionally, ANFR achieves a balance between privacy and utility when training with differential privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for devices to work together on a task without sharing their data. But this process can be tricky because the devices might have different types of data. To solve this problem, researchers created an approach called Adaptive Normalization-free Feature Recalibration (ANFR). This method helps devices learn from each other better by normalizing the information they use and paying attention to the most important features. ANFR works well with different methods for combining device results and is useful in both general and personalized learning situations. It’s also good at keeping data private while still allowing devices to learn. |
Keywords
» Artificial intelligence » Attention » Federated learning
