| 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 | 6457 | 2021 |
| Learning from history for byzantine robust optimization SP Karimireddy, L He, M Jaggi ICML 2021 - International Conference on Machine Learning, 5311-5319, 2021 | 192 | 2021 |
| Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing SP Karimireddy*, L He*, M Jaggi ICLR 2022 - International Conference on Learning Representations, 2020 | 172* | 2020 |
| Cola: Decentralized linear learning L He*, A Bian*, M Jaggi NeurIPS 2018 - Conference on Neural Information Processing Systems, 4541–4551, 2018 | 155 | 2018 |
| Secure byzantine-robust machine learning L He, SP Karimireddy, M Jaggi NeurIPS Workshop 2020 (SPICY-FL) Conference on Neural Information Processing …, 2020 | 67 | 2020 |
| 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 | 65 | 2021 |
| Byzantine-robust decentralized learning via clippedgossip L He, SP Karimireddy, M Jaggi arXiv preprint arXiv:2202.01545, 2022 | 50* | 2022 |
| Provably Personalized and Robust Federated Learning M Werner, L He, SP Karimireddy, M Jordan, M Jaggi TMLR 2023, 2023 | 11* | 2023 |
| Debiasing Conditional Stochastic Optimization L He, SP Kasiviswanathan NeurIPS 2023 - Conference on Neural Information Processing Systems, 2023 | 5 | 2023 |
| CoBo: Collaborative Learning via Bilevel Optimization D Hashemi, L He, M Jaggi NeurIPS 2024 - Conference on Neural Information Processing Systems, 2024 | | 2024 |
| Distributed Optimization with Byzantine Robustness Guarantees L He EPFL, 2023 | | 2023 |
Martin JaggiEPFLEmail verificata su epfl.ch