Sustainability in Federated Learning

Kai Li (), Xin Yuan () and Wei Ni ()
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Kai Li: University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust (SnT)
Xin Yuan: Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Business Unit
Wei Ni: Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Business Unit

Chapter 12 in Security and Resilience in Distributed Machine Learning, 2026, pp 225-234 from Springer

Abstract: Abstract FL can reduce data-transfer volumes and mitigate privacy risks by pushing computation to the edge; however, it may also increase energy use and carbon emissions if naively orchestrated across heterogeneous devices and networks. This chapter formalizes sustainability objectives for FL, connects them to the United Nations Sustainable Development Goals (UN SDGs), and develops concrete, measurable strategies for energy- and carbon-aware FL pipelines. We conclude with a robustness module based on Representational Similarity Analysis (RSA) that prevents wasteful training under data poisoning, thereby improving both accuracy and sustainability.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-032-23959-4_12

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DOI: 10.1007/978-3-032-23959-4_12

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