Person
Lie He
This person is no longer with EPFL
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Related publications (4)
Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.
Distributed Optimization with Byzantine Robustness Guarantees
As modern machine learning continues to achieve unprecedented benchmarks, the resource demands to train these advanced models grow drastically. This has led to a paradigm shift towards distributed training. However, the presence of adversariesâwhether ma ...
EPFL2023Advances and Open Problems in Federated Learning
Martin Jaggi, Sebastian Urban Stich, Lie He, Yang Liu, Ayfer Özgür Aydin, Florian Tramèr, Qiang Yang, Ananda Theertha Suresh, Badih Ghazi
Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decen ...
NOW PUBLISHERS INC2021Learning from History for Byzantine Robust Optimization
Martin Jaggi, Lie He, Sai Praneeth Reddy Karimireddy
Byzantine robustness has received significant attention recently given its importance for distributed and federated learning. In spite of this, we identify severe flaws in existing algorithms even when the data across the participants is identically distri ...
JMLR-JOURNAL MACHINE LEARNING RESEARCH2021