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Consensus Based Multi-Layer Perceptrons for Edge Computing

arXiv 2102.05021 ↗First posted 9 Feb 2021cs.LG cs.DC cs.NEcited by 0

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Abstract (arXiv)

In recent years, storing large volumes of data on distributed devices has become commonplace. Applications involving sensors, for example, capture data in different modalities including image, video, audio, GPS and others. Novel algorithms are required to learn from this rich distributed data. In this paper, we present consensus based multi-layer perceptrons for resource-constrained devices. Assuming nodes (devices) in the distributed system are arranged in a graph and contain vertically partitioned data, the goal is to learn a global function that minimizes the loss. Each node learns a feed-forward multi-layer perceptron and obtains a loss on data stored locally. It then gossips with a neighbor, chosen uniformly at random, and exchanges information about the loss. The updated loss is used to run a back propagation algorithm and adjust weights appropriately. This method enables nodes to learn the global function without exchange of data in the network. Empirical results reveal that the consensus algorithm converges to the centralized model and has performance comparable to centralized multi-layer perceptrons and tree-based algorithms including random forests and gradient boosted decision trees.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 9 Feb 2021.

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References · 15

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3 more
  1. 2016Online Decorrelation of Humidity and Temperature in Chemical Sensors for Continuous Monitoringcited by 8
  2. 2018GoSGD: Distributed Optimization for Deep Learning with Gossip Exchangecited by 6
  3. 2016Fast Incremental Learning for Off-Road Robot Navigationcited by 1

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