Algorithms for Distributed and Collaborative Deep Learning
Event details
| Date | 16.11.2023 |
| Hour | 13:30 › 14:30 |
| Speaker | Anastasiia Koloskova, graduating doctoral student in Machine Learning and Optimization Laboratory, EPFL |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
EDIC Mock interview public talk announcement to prepare for hiring interviews within academia
Abstract
In distributed learning, multiple workers (e.g., GPUs) contribute in parallel to expedite the training of machine learning models. In collaborative learning, the training data is distributed among several participants due to the privacy-sensitive nature of the data. These participants collaborate together to solve a common machine learning task. In this talk, I will discuss various challenges encountered in both scenarios, including communication efficiency, data heterogeneity, and privacy protection of the training data.
Bio
I am a PhD student at EPFL at the laboratory of Optimization and Machine Learning with Prof. Martin Jaggi. My research is focused on distributed optimization for machine learning and collaborative learning. During my studies I was awarded a Google PhD Fellowship in Machine Learning.
Abstract
In distributed learning, multiple workers (e.g., GPUs) contribute in parallel to expedite the training of machine learning models. In collaborative learning, the training data is distributed among several participants due to the privacy-sensitive nature of the data. These participants collaborate together to solve a common machine learning task. In this talk, I will discuss various challenges encountered in both scenarios, including communication efficiency, data heterogeneity, and privacy protection of the training data.
Bio
I am a PhD student at EPFL at the laboratory of Optimization and Machine Learning with Prof. Martin Jaggi. My research is focused on distributed optimization for machine learning and collaborative learning. During my studies I was awarded a Google PhD Fellowship in Machine Learning.
Practical information
- General public
- Free