Communication Efficient Decentralized Training with Multiple Local Updates
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Communication efficiency plays a significant role in decentralized optimization, especially when the data is highly non-identically distributed. In this paper, we propose a novel algorithm that we call Periodic Decentralized SGD (PD-SGD), to reduce the communication cost in a decentralized heterogeneous network. PD-SGD alternates between multiple local updates and multiple decentralized communications, making communication more flexible and controllable. We theoretically prove PD-SGD convergence at speed O(1/√(nT)) under the setting of stochastic non-convex optimization and non-i.i.d. data where n is the number of worker nodes. We also propose a novel decay strategy which periodically shrinks the length of local updates. PD-SGD equipped with this strategy can better balance the communication-convergence trade-off both theoretically and empirically.
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