On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning

• URL: http://arxiv.org/abs/2201.11803v1
• Date: Thu, 27 Jan 2022 20:43:38 GMT
• Title: On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning
• Authors: Hanhan Zhou, Tian Lan, Guru Venkataramani, Wenbo Ding
• Abstract summary: We present a unifying framework for heterogeneous FL algorithms with em arbitrary adaptive online model pruning. In particular, we prove that under certain sufficient conditions, these algorithms converge to a stationary point of standard FL for general smooth cost functions. We illuminate two key factors impacting convergence: pruning-induced noise and minimum coverage index.
• Score: 15.300983585090794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the biggest challenges in Federated Learning (FL) is that client devices often have drastically different computation and communication resources for local updates. To this end, recent research efforts have focused on training heterogeneous local models obtained by pruning a shared global model. Despite empirical success, theoretical guarantees on convergence remain an open question. In this paper, we present a unifying framework for heterogeneous FL algorithms with {\em arbitrary} adaptive online model pruning and provide a general convergence analysis. In particular, we prove that under certain sufficient conditions and on both IID and non-IID data, these algorithms converges to a stationary point of standard FL for general smooth cost functions, with a convergence rate of $O(\frac{1}{\sqrt{Q}})$. Moreover, we illuminate two key factors impacting convergence: pruning-induced noise and minimum coverage index, advocating a joint design of local pruning masks for efficient training.

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