University of Oulu

D. Wen, M. Bennis and K. Huang, "Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning," in IEEE Transactions on Wireless Communications, vol. 19, no. 12, pp. 8272-8286, Dec. 2020, doi: 10.1109/TWC.2020.3021177

Joint parameter-and-bandwidth allocation for improving the efficiency of partitioned edge learning

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Author: Wen, Dingzhu1; Bennis, Mehdi2; Huang, Kaibin1
Organizations: 1The University of Hong Kong, Hong Kong
2University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-01-22


To leverage data and computation capabilities of mobile devices, machine learning algorithms are deployed at the network edge for training artificial intelligence (AI) models, resulting in the new paradigm of edge learning. In this paper, we consider the framework of partitioned edge learning for iteratively training a large-scale model using many resource-constrained devices (called workers). To this end, in each iteration, the model is dynamically partitioned into parametric blocks, which are downloaded to worker groups for updating using data subsets. Then, the local updates are uploaded to and cascaded by the server for updating a global model. To reduce resource usage by minimizing the total learning-and-communication latency, this work focuses on the novel joint design of parameter (computation load) allocation and bandwidth allocation (for downloading and uploading). Two design approaches are adopted. First, a practical sequential approach, called partially integrated parameter-and-bandwidth allocation (PABA), yields two schemes, namely bandwidth aware parameter allocation and parameter aware bandwidth allocation. The former minimizes the load for the slowest (in computing) of worker groups, each training a same parametric block. The latter allocates the largest bandwidth to the worker being the latency bottleneck. Second, PABA are jointly optimized. Despite it being a nonconvex problem, an efficient and optimal solution algorithm is derived by intelligently nesting a bisection search and solving a convex problem. Experimental results using real data demonstrate that integrating PABA can substantially improve the performance of partitioned edge learning in terms of latency (by e.g., 46%) and accuracy (by e.g., 4% given the latency of 100 seconds).

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Series: IEEE transactions on wireless communications
ISSN: 1536-1276
ISSN-E: 1558-2248
ISSN-L: 1536-1276
Volume: 19
Issue: 12
Pages: 8272 - 8286
DOI: 10.1109/TWC.2020.3021177
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: The work of K. Huang and D. Wen was supported by Hong Kong Research Grants Council under Grants 17208319 and 17209917, Innovation and Technology Fund under Grant GHP/016/18GD, and Guang-dong Basic and Applied Basic Research Foundation under Grant 2019B1515130003. The work of M. Bennis was supported by EU-CHISTERA projects CONNECT and LeadingEdge.
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