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BIMSA Optimization Seminar
Handling Device Heterogeneity in Federated Learning: The First Optimal Parallel SGD in the Presence of Data, Compute, and Communication Heterogeneity
Handling Device Heterogeneity in Federated Learning: The First Optimal Parallel SGD in the Presence of Data, Compute, and Communication Heterogeneity
组织者
演讲者
时间
2024年11月25日 10:00 至 11:00
地点
Auditorium, SIMIS
摘要
演讲者介绍
Peter Richtárik is a professor of Computer Science at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, where he leads the Optimization and Machine Learning Lab. His research interests lie at the intersection of mathematics, computer science, machine learning, optimization, numerical linear algebra, and high-performance computing. Through his work on randomized and distributed optimization algorithms, he has contributed to the foundations of machine learning, optimization, and randomized numerical linear algebra. He is one of the original developers of Federated Learning. Prof. Richtárik’s works attracted international awards, including the Charles Broyden Prize, SIAM SIGEST Best Paper Award, Distinguished Speaker Award at the 2019 International Conference on Continuous Optimization, the IMA Leslie Fox Prize (three times), and a Best Paper Award at the NeurIPS 2020 Workshop on Scalability, Privacy, and Security in Federated Learning. Several of his works are among the most read papers published by the SIAM Journal on Optimization and the SIAM Journal on Matrix Analysis and Applications. Prof. Richtárik serves as an Area Chair for leading machine learning conferences, including NeurIPS, ICML, and ICLR, and is an Action Editor of JMLR, and Associate Editor of Numerische Mathematik and Optimization Methods and Software. In the past, he served as an Action Editor of TMLR and an Area Editor of JOTA.