Abstract
This paper addresses the issue of communication overhead costs of federated learning including transmission bandwidth and synchronisation efforts. These costs typically consist of locally observable costs on executing components, but there are also hidden costs that can only be measured from a system-wide perspective. The goal is to provide insight into these hidden costs, measure them and identify strategies for reducing them. We propose an approach to tackle the hidden costs by establishing a methodology consisting of an eavesdrop** concept and an evaluation strategy. This way we obtain a refined analysis of directly observable costs contrasting hidden costs, which is underpinned by experiments based on a 40-client-spanning federated learning system and the FEMNIST dataset.
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Acknowledgements
S3AI is a COMET Module within the COMET - Competence Centers for Excellent Technologies Programme and funded by BMK, BMAW and the State of Upper Austria. The COMET Programme is managed by FFG. The research reported in this paper has been funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), the Federal Ministry for Labour and Economy (BMAW), and the State of Upper Austria in the frame of the COMET Module Security and Safety for Shared Artificial Intelligence by Deep Model Design (S3AI) [(FFG grant no. 872172) and the SCCH competence center INTEGRATE [(FFG grant no. 892418)] within the COMET - Competence Centers for Excellent Technologies Programme managed by Austrian Research Promotion Agency FFG.
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Meindl, R., Moser, B.A. (2023). Measuring Overhead Costs of Federated Learning Systems by Eavesdrop**. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2023 Workshops. DEXA 2023. Communications in Computer and Information Science, vol 1872. Springer, Cham. https://doi.org/10.1007/978-3-031-39689-2_4
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DOI: https://doi.org/10.1007/978-3-031-39689-2_4
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