Analysis of Federated Learning as a Distributed Solution for Learning on Edge Devices

Lameh, Saba F.; Noble, Wade; Amannejad, Yasaman; Afshar, Arash

doi:10.1109/idsta50958.2020.9264060

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…There is also an additional opportunity with federated learning and privacy-preserving machine learning to collaborate across organisations and share results, as well as models. Lameh et al (2020) explain the introduction of federated learning by McMahan et al (2017) as "a decentralized ML approach suitable for edge computing", with an ability to respond to processing needs. This will bring the ability to advance the wind energy field without the concerns regarding data privacy.…”

Section: Data Analyticsmentioning

confidence: 99%

Grand challenges in the digitalisation of wind energy

Clifton

Barber

Bray³

et al. 2023

Wind Energ. Sci.

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Abstract. The availability of large amounts of data is starting to impact how the wind energy community works. From turbine design to plant layout, construction, commissioning, and maintenance and operations, new processes and business models are springing up. This is the process of digitalisation, and it promises improved efficiency and greater insight, ultimately leading to increased energy capture and significant savings for wind plant operators, thus reducing the levelised cost of energy. Digitalisation is also impacting research, where it is both easing and speeding up collaboration, as well as making research results more accessible. This is the basis for innovations that can be taken up by end users. But digitalisation faces barriers. This paper uses a literature survey and the results from an expert elicitation to identify three common industry-wide barriers to the digitalisation of wind energy. Comparison with other networked industries and past and ongoing initiatives to foster digitalisation show that these barriers can only be overcome by wide-reaching strategic efforts, and so we see these as “grand challenges” in the digitalisation of wind energy. They are, first, creating FAIR data frameworks; secondly, connecting people and data to foster innovation; and finally, enabling collaboration and competition between organisations. The grand challenges in the digitalisation of wind energy thus include a mix of technical, cultural, and business aspects that will need collaboration between businesses, academia, and government to solve. Working to mitigate them is the beginning of a dynamic process that will position wind energy as an essential part of a global clean energy future.

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Section: Data Analyticsmentioning

confidence: 99%