Search Results for author: Lie He

Found 8 papers, 6 papers with code

Byzantine-Robust Decentralized Learning via Self-Centered Clipping

1 code implementation3 Feb 2022 Lie He, Sai Praneeth Karimireddy, Martin Jaggi

In this paper, we study the challenging task of Byzantine-robust decentralized training on arbitrary communication graphs.

Federated Learning

RelaySum for Decentralized Deep Learning on Heterogeneous Data

1 code implementation NeurIPS 2021 Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi

A key challenge, primarily in decentralized deep learning, remains the handling of differences between the workers' local data distributions.

Learning from History for Byzantine Robust Optimization

1 code implementation18 Dec 2020 Sai Praneeth Karimireddy, Lie He, Martin Jaggi

Secondly, we prove that even if the aggregation rules may succeed in limiting the influence of the attackers in a single round, the attackers can couple their attacks across time eventually leading to divergence.

Federated Learning Stochastic Optimization

Byzantine-Robust Learning on Heterogeneous Datasets via Resampling

no code implementations28 Sep 2020 Lie He, Sai Praneeth Karimireddy, Martin Jaggi

In Byzantine-robust distributed optimization, a central server wants to train a machine learning model over data distributed across multiple workers.

Distributed Optimization

Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing

1 code implementation ICLR 2022 Sai Praneeth Karimireddy, Lie He, Martin Jaggi

In Byzantine robust distributed or federated learning, a central server wants to train a machine learning model over data distributed across multiple workers.

Distributed Optimization Federated Learning

Secure Byzantine-Robust Machine Learning

no code implementations8 Jun 2020 Lie He, Sai Praneeth Karimireddy, Martin Jaggi

Increasingly machine learning systems are being deployed to edge servers and devices (e. g. mobile phones) and trained in a collaborative manner.

COLA: Decentralized Linear Learning

1 code implementation NeurIPS 2018 Lie He, An Bian, Martin Jaggi

Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy.

General Classification

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