Peter Richtárik
KAUST
VOL_04
22 June 2022 at 5 pm
online & Twin City C, Bratislava
The role of local training in federated learning: We finally know what’s going on!
Ever wanted to start utilizing federated learning? Professor Peter Richtarik from King Abdullah University of Science and Technology will give us a detailed lecture on the role of local training in federated learning and will answer any questions you may have.
Federated Learning refers to machine learning over private data stored across a large number of heterogeneous devices, such as mobile phones or hospitals. In an October 2020 Forbes article, and alongside self-supervised learning and transformers, Federated Learning was described as one of three emerging areas that will shape the next generation of AI technologies. In this talk I will explain how we recently resolved one of the key open algorithmic and mathematical problems in this field.
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Peter Richtárik is a professor of Computer Science at KAUST, Saudi Arabia, where he leads the Optimization and Machine Learning Lab. Through his work on randomized and distributed algorithms, he has contributed to the foundations of optimization, machine learning, and federated learning. Prof Richtárik’s serves as an Area Chair for leading machine learning conferences, including NeurIPS, ICML and ICLR, and is the recipient of numerous international awards.
_Agenda
Language: English
5:00 pm
Welcome
5:10 pm
Interactive presentation - The role of local training in federated learning: we finally know what's going on! - Peter Richtarik (KAUST)
6:10 pm
Live Q&A: _Slido #better_ai
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Past Meetups
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The role of local training in federated learning: We finally know what’s going on!
Ever wanted to start utilizing federated learning? Professor Peter Richtarik from King Abdullah University of Science and Technology will give us a detailed lecture on the role of local training in federated learning and will answer any questions you may have.
Federated Learning refers to machine learning over private data stored across a large number of heterogeneous devices, such as mobile phones or hospitals. In an October 2020 Forbes article, and alongside self-supervised learning and transformers, Federated Learning was described as one of three emerging areas that will shape the next generation of AI technologies. In this talk I will explain how we recently resolved one of the key open algorithmic and mathematical problems in this field.
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