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Lie He

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Related publications (5)

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Distributed Optimization with Byzantine Robustness Guarantees

Lie He

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 adversariesâ whether ma ...

EPFL2023

COLA: Decentralized Linear Learning

Martin Jaggi, Lie He

Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over ma ...

2018

Advances and Open Problems in Federated Learning

Martin Jaggi, Ayfer Özgür Aydin, Sebastian Urban Stich, Florian Tramèr, Lie He

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 INC2021

CoBo: Collaborative Learning via Bilevel Optimization

Martin Jaggi, Lie He

Collaborative learning is an important tool to train multiple clients more effectively by enabling communication among clients. Identifying helpful clients, however, presents challenging and often introduces significant overhead. In this paper, we model cl ...

2024

Learning from History for Byzantine Robust Optimization

Martin Jaggi, Sai Praneeth Reddy Karimireddy, Lie He

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