Summary of Decentralized Optimization in Time-varying Networks with Arbitrary Delays, by Tomas Ortega et al.
Decentralized Optimization in Time-Varying Networks with Arbitrary Delays
by Tomas Ortega, Hamid Jafarkhani
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Systems and Control (eess.SY); Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 A decentralized optimization problem is tackled for networks affected by communication delays, which is crucial in collaborative machine learning, sensor networks, and multi-agent systems. To address this challenge, virtual non-computing nodes are added to the network, resulting in directed graphs. The existing solutions assume nodes know their out-degrees, limiting their applicability. A novel gossip-based algorithm, called DT-GO, is introduced that does not require knowing the out-degrees. This algorithm is applicable to general directed networks with delays or limited acknowledgment capabilities. Convergence rates are derived for both convex and non-convex objectives, showing that DT-GO achieves the same complexity order as centralized Stochastic Gradient Descent. The effects of the graph topology and delays are confined to higher-order terms. Additionally, time-varying network topologies are accommodated in the analysis. Numerical simulations support the theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decentralized optimization is important for networks with communication delays. To solve this problem, we add virtual nodes to the network, making it a directed graph. Most solutions assume you know how many edges point away from each node (out-degrees), but this isn’t always possible. We introduce a new algorithm called DT-GO that doesn’t need out-degree information. This works for general directed networks with delays or limited feedback. We show that DT-GO is just as good as the best centralized method, and we can even handle changing network topologies. Our results are supported by computer simulations. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Stochastic gradient descent
