FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems

• URL: http://arxiv.org/abs/2306.05172v4
• Date: Mon, 04 Nov 2024 08:53:48 GMT
• Title: FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
• Authors: Herbert Woisetschläger, Alexander Erben, Ruben Mayer, Shiqiang Wang, Hans-Arno Jacobsen,
• Abstract summary: Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. In this paper, we propose FLEdge, which complements existing FL benchmarks by enabling a systematic evaluation of client capabilities.
• Score: 61.335229621081346
• License:
• Abstract: Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. Heterogeneous hardware, unreliable client devices, and energy constraints often characterize edge computing systems. In this paper, we propose FLEdge, which complements existing FL benchmarks by enabling a systematic evaluation of client capabilities. We focus on computational and communication bottlenecks, client behavior, and data security implications. Our experiments with models varying from 14K to 80M trainable parameters are carried out on dedicated hardware with emulated network characteristics and client behavior. We find that state-of-the-art embedded hardware has significant memory bottlenecks, leading to 4x longer processing times than on modern data center GPUs.

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