Summary of Fine-tuning Personalization in Federated Learning to Mitigate Adversarial Clients, by Youssef Allouah et al.
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
by Youssef Allouah, Abdellah El Mrini, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 abstract discusses federated learning (FL), a machine learning approach that enables multiple machines or devices to learn collectively while keeping their data private. The paper focuses on personalization in FL settings, where some clients may be adversarial. It analyzes the performance of an interpolated personalized FL framework in the presence of such clients and determines when full collaboration performs worse than fine-tuned personalization. The authors characterize situations where full collaboration fails due to data heterogeneity and the number of adversarial clients. They support their findings with experiments on mean estimation, binary classification, and image classification tasks using synthetic and benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for lots of devices to learn together while keeping their own information private. Sometimes, this can lead to bad results because some devices have very different data. Personalization helps fix this by giving each device its own special model that’s just right for it. But what if some devices are trying to cause trouble? The paper looks at how well personalization works when some devices are being sneaky and tries to figure out when everyone working together isn’t the best way. |
Keywords
» Artificial intelligence » Classification » Federated learning » Image classification » Machine learning
