Summary of Communication-efficient Distributed Deep Learning Via Federated Dynamic Averaging, by Michail Theologitis et al.
Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging
by Michail Theologitis, Georgios Frangias, Georgios Anestis, Vasilis Samoladas, Antonios Deligiannakis
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 paper proposes Federated Dynamic Averaging (FDA), a novel distributed deep learning technique that efficiently synchronizes models in federated settings by dynamically triggering synchronization based on model variance. By only updating the global model when local models have significantly diverged, FDA reduces communication costs by orders of magnitude compared to traditional and cutting-edge algorithms. The paper demonstrates FDA’s effectiveness across various learning tasks and data heterogeneity settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way for computers to work together to train artificial intelligence models. Normally, these models need to be shared between devices, which takes up a lot of bandwidth and makes it hard to use in places with slow internet. The new method, called Federated Dynamic Averaging, only shares information when the local models have changed a lot from each other. This makes it much faster and more practical for use in different settings. |
Keywords
» Artificial intelligence » Deep learning
