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Machine learning builds on the collection and processing of data. Since the data often are collected by mobile phones or internet-of-things devices, they must be transferred wirelessly to enable machine learning. In this episode, Emil Björnson and Erik G. Larsson are visited by Carlo Fischione, a Professor at the KTH Royal Institute of Technology. The conversation circles around distributed machine learning and how the wireless technology can evolve to support learning applications via network slicing, information-aware communication, and over-the-air computation. To learn more, they recommend the article “Wireless for Machine Learning” (https://arxiv.org/abs/2008.13492). Carlo’s website is https://people.kth.se/~carlofi/ and the Machine Learning for Communications ETI has the website https://mlc.committees.comsoc.org. Music: On the Verge by Joseph McDade. Visit Erik’s website https://liu.se/en/employee/erila39 and Emil’s website https://ebjornson.com/
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Machine learning builds on the collection and processing of data. Since the data often are collected by mobile phones or internet-of-things devices, they must be transferred wirelessly to enable machine learning. In this episode, Emil Björnson and Erik G. Larsson are visited by Carlo Fischione, a Professor at the KTH Royal Institute of Technology. The conversation circles around distributed machine learning and how the wireless technology can evolve to support learning applications via network slicing, information-aware communication, and over-the-air computation. To learn more, they recommend the article “Wireless for Machine Learning” (https://arxiv.org/abs/2008.13492). Carlo’s website is https://people.kth.se/~carlofi/ and the Machine Learning for Communications ETI has the website https://mlc.committees.comsoc.org. Music: On the Verge by Joseph McDade. Visit Erik’s website https://liu.se/en/employee/erila39 and Emil’s website https://ebjornson.com/
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