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Summary of Distributed Event-based Learning Via Admm, by Guner Dilsad Er et al.


Distributed Event-Based Learning via ADMM

by Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach

First submitted to arxiv on: 17 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)

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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 presents a distributed learning algorithm that minimizes a global objective function by exchanging information among agents over a network. The key features of the approach are its ability to reduce communication by only triggering it when necessary and its agnosticism towards data distributions among agents, guaranteeing convergence even in scenarios with arbitrarily distinct local data distributions. The algorithm’s convergence rate is analyzed in both convex and nonconvex settings, with accelerated rates derived for the convex case. The paper also explores the effects of communication failures and demonstrates the robustness of the algorithm to these. Numerical experiments on MNIST and CIFAR-10 datasets show significant communication savings (35% or more), resilience towards heterogeneous data distributions, and improved performance compared to common baselines such as FedAvg, FedProx, SCAFFOLD, and FedADMM.
Low GrooveSquid.com (original content) Low Difficulty Summary
The paper is about a way for machines to learn from each other by sharing information over the internet. The method is special because it only shares information when needed, which helps reduce how much data needs to be shared. It also works even if the machines have different types of data. The paper shows that this approach can make learning faster and more efficient, especially in situations where there are communication problems or lots of different data. This was tested on two big datasets (MNIST and CIFAR-10) and showed significant improvements over other common methods.

Keywords

» Artificial intelligence  » Objective function