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Learning-Driven Decentralized Machine Learning in Resource-Constrained Wireless Edge Computing
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DOI：10.1109/INFOCOM42981.2021.9488817.png)
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Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing. To fully utilize the widely distributed data, we concentrate on a wireless edge computing system that conducts model training using decentralized peer-to-peer (P2P) methods. However, there are two major challenges on the way towards efficient P2P model training: limited resources (e.g., network bandwidth and battery life of mobile edge devices) and time-varying network connectivity due to device mobility or wireless channel dynamics, which have received less attention in recent years. To address these two challenges, this paper adaptively constructs a dynamic and efficient P2P topology, where model aggregation occurs at the edge devices. In a nutshell, we first formulate the topology construction for P2P learning (TCPL) problem with resource constraints as an integer programming problem. Then a learning-driven method is proposed to adaptively construct a topology at each training epoch. We further give the convergence analysis on training machine learning models even with non-convex loss functions. Extensive simulation results show that our proposed method can improve the model training efficiency by about 11% with resource constraints and reduce the communication cost by about 30% under the same accuracy requirement compared to the benchmarks.
Keyword:
Edge Computing
Distributed Machine Learning
Peer-to-Peer
Resource Allocation
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