Publication

Democratizing Machine Learning

Alexandre David Olivier Maurer, Nirupam Gupta, Rafaël Benjamin Pinot
2022
Conference Papers

Abstract

The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional distributed learning algorithms impose a uniform workload on all participating devices, most often discarding the weakest participants. This not only induces a suboptimal use of available computational resources, but also significantly reduces the quality of the learning process, as data held by the slowest devices is discarded from the procedure.

Official source

https://infoscience.epfl.ch/entities/publication/a0fc7483-ed8a-4495-aced-4bff9d22465f

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Alexandre David Olivier Maurer, Nirupam Gupta, Rafaël Benjamin Pinot
2022
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a0fc7483-ed8a-4495-aced-4bff9d22465f

About this result

Related concepts (32)

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

Learning

Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single event (e.g. being burned by a hot stove), but much skill and knowledge accumulate from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

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