RelaySum for Decentralized Deep Learning on Heterogeneous Data
This addresses the problem of data heterogeneity in decentralized deep learning for workers in networks without all-to-all connectivity, offering a novel solution with proven scalability and accuracy.
The paper tackles the challenge of handling heterogeneous data distributions in decentralized deep learning by introducing the RelaySum mechanism, which uses spanning trees to distribute information uniformly across workers with finite delays, and proves that RelaySGD based on this mechanism is independent of data heterogeneity and scales to many workers, enabling highly accurate training.
In decentralized machine learning, workers compute model updates on their local data. Because the workers only communicate with few neighbors without central coordination, these updates propagate progressively over the network. This paradigm enables distributed training on networks without all-to-all connectivity, helping to protect data privacy as well as to reduce the communication cost of distributed training in data centers. A key challenge, primarily in decentralized deep learning, remains the handling of differences between the workers' local data distributions. To tackle this challenge, we introduce the RelaySum mechanism for information propagation in decentralized learning. RelaySum uses spanning trees to distribute information exactly uniformly across all workers with finite delays depending on the distance between nodes. In contrast, the typical gossip averaging mechanism only distributes data uniformly asymptotically while using the same communication volume per step as RelaySum. We prove that RelaySGD, based on this mechanism, is independent of data heterogeneity and scales to many workers, enabling highly accurate decentralized deep learning on heterogeneous data. Our code is available at http://github.com/epfml/relaysgd.