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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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GrooveSquid.com Paper Summaries

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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