On the Effect of Consensus in Decentralized Deep Learning
Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works revealed that decentralized training often suffers from generalization issues: the performance of models trained in a decentralized fashion is in general worse than the performance of models trained in a centralized fashion, and this generalization gap is impacted by parameters such as network size, communication topology, and data partitioning. We identify the changing consensus distance between devices as a key parameter to explain the gap between centralized and decentralized training. We show that when the consensus distance does not grow too large, the performance of centralized training can be reached and sometimes surpassed. We highlight the intimate interplay between network topology and learning rate at the different training phases and discuss the implications for communication efficient training schemes. Our insights into the generalization gap in decentralized deep learning allow the principled design of better training schemes that mitigate these effects.
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CIFAR-10
ImageNet-32
