Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD


  • Jiayi Wang University of Utah
  • Shiqiang Wang IBM Research
  • Rong-Rong Chen University of Utah
  • Mingyue Ji University of Utah



Machine Learning (ML)


Hierarchical SGD (H-SGD) has emerged as a new distributed SGD algorithm for multi-level communication networks. In H-SGD, before each global aggregation, workers send their updated local models to local servers for aggregations. Despite recent research efforts, the effect of local aggregation on global convergence still lacks theoretical understanding. In this work, we first introduce a new notion of "upward" and "downward" divergences. We then use it to conduct a novel analysis to obtain a worst-case convergence upper bound for two-level H-SGD with non-IID data, non-convex objective function, and stochastic gradient. By extending this result to the case with random grouping, we observe that this convergence upper bound of H-SGD is between the upper bounds of two single-level local SGD settings, with the number of local iterations equal to the local and global update periods in H-SGD, respectively. We refer to this as the "sandwich behavior". Furthermore, we extend our analytical approach based on "upward" and "downward" divergences to study the convergence for the general case of H-SGD with more than two levels, where the "sandwich behavior" still holds. Our theoretical results provide key insights of why local aggregation can be beneficial in improving the convergence of H-SGD.




How to Cite

Wang, J., Wang, S., Chen, R.-R., & Ji, M. (2022). Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD. Proceedings of the AAAI Conference on Artificial Intelligence, 36(8), 8548-8556.



AAAI Technical Track on Machine Learning III