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Lie He
Lie He
PhD student in CS, EPFL
Verified email at epfl.ch
Title
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Cited by
Year
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
Foundations and trends® in machine learning 14 (1–2), 1-210, 2021
54722021
Learning from history for byzantine robust optimization
SP Karimireddy, L He, M Jaggi
ICML 2021 - International Conference on Machine Learning, 5311-5319, 2021
1622021
Cola: Decentralized linear learning
L He*, A Bian*, M Jaggi
NeurIPS 2018 - Conference on Neural Information Processing Systems, 4541–4551, 2018
1492018
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
SP Karimireddy*, L He*, M Jaggi
ICLR 2022 - International Conference on Learning Representations, 2020
138*2020
Secure byzantine-robust machine learning
L He, SP Karimireddy, M Jaggi
NeurIPS Workshop 2020 (SPICY-FL) Conference on Neural Information Processing …, 2020
612020
RelaySum for Decentralized Deep Learning on Heterogeneous Data
T Vogels*, L He*, A Koloskova, T Lin, SP Karimireddy, SU Stich, M Jaggi
NeurIPS 2021 - Conference on Neural Information Processing Systems, 2021
592021
Byzantine-robust decentralized learning via clippedgossip
L He, SP Karimireddy, M Jaggi
arXiv preprint arXiv:2202.01545, 2022
37*2022
Provably Personalized and Robust Federated Learning
M Werner, L He, SP Karimireddy, M Jordan, M Jaggi
TMLR 2023, 2023
3*2023
Debiasing Conditional Stochastic Optimization
L He, SP Kasiviswanathan
NeurIPS 2023 - Conference on Neural Information Processing Systems, 2023
32023
Distributed Optimization with Byzantine Robustness Guarantees
L He
EPFL, 2023
2023
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Articles 1–10