Summary of Unsupervised Federated Optimization at the Edge: D2d-enabled Learning Without Labels, by Satyavrat Wagle et al.
Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels
by Satyavrat Wagle, Seyyedali Hosseinalipour, Naji Khosravan, Christopher G. Brinton
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Federated learning (FL) is a key solution for distributed machine learning (ML). Traditional FL focuses on supervised ML tasks, but many applications require learning from unlabeled data. To address this, we introduce Cooperative Federated unsupervised Contrastive Learning ({}) to facilitate FL across edge devices with unlabeled datasets. {} leverages local device cooperation through explicit or implicit information exchange via device-to-device (D2D) communications to improve local diversity. We propose a “smart information push-pull” methodology for data/embedding exchange, tailored to FL settings with soft or strict data privacy restrictions. Numerical evaluations demonstrate that {} aligns latent spaces learned across devices, enables faster global model training, and is effective in extreme non-i.i.d. data distribution settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have many computers or devices that want to work together to learn from a large dataset. But each device only has a small piece of the data, and it’s not labeled (like pictures with names). Federated learning is a way for these devices to share information and learn together without sharing their entire datasets. This paper introduces a new method called Cooperative Federated unsupervised Contrastive Learning that helps devices work together better when they have unlabeled data. It allows them to exchange information and improve their learning. This can be useful in many situations, like when devices are connected through the internet. |
Keywords
» Artificial intelligence » Embedding » Federated learning » Machine learning » Supervised » Unsupervised
