| 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 | 9933 | 2021 |
| SCAFFOLD: Stochastic Controlled Averaging for Federated Learning SP Karimireddy, S Kale, M Mohri, SJ Reddi, SU Stich, AT Suresh ICML 2020 - International Conference on Machine Learning, 2019 | 4957 | 2019 |
| Ensemble Distillation for Robust Model Fusion in Federated Learning T Lin, L Kong, SU Stich, M Jaggi NeurIPS 2020 - Advances in Neural Information Processing Systems 33, 2020 | 1626 | 2020 |
| Local SGD Converges Fast and Communicates Little SU Stich ICLR 2019 - International Conference on Learning Representations, 2019 | 1434 | 2019 |
| Sparsified SGD with memory SU Stich, JB Cordonnier, M Jaggi NeurIPS 2018 - Advances in Neural Information Processing Systems, 4448-4459, 2018 | 1140 | 2018 |
| Error Feedback Fixes SignSGD and other Gradient Compression Schemes SP Karimireddy, Q Rebjock, SU Stich, M Jaggi ICML 2019 - International Conference on Machine Learning, 2019 | 714 | 2019 |
| A Unified Theory of Decentralized SGD with Changing Topology and Local Updates A Koloskova, N Loizou, S Boreiri, M Jaggi, SU Stich ICML 2020 - International Conference on Machine Learning, 2020 | 705 | 2020 |
| Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication A Koloskova, SU Stich, M Jaggi ICML 2019 - International Conference on Machine Learning, 2019 | 671 | 2019 |
| Don't Use Large Mini-Batches, Use Local SGD T Lin, SU Stich, KK Patel, M Jaggi ICLR 2020 - International Conference on Learning Representations, 2020 | 557 | 2020 |
| A Field Guide to Federated Optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021 | 516 | 2021 |
| Breaking the centralized barrier for cross-device federated learning SP Karimireddy, M Jaggi, S Kale, M Mohri, S Reddi, SU Stich, AT Suresh NeurIPS 2021 - Advances in Neural Information Processing Systems 34, 2021 | 398* | 2021 |
| The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates SU Stich, SP Karimireddy Journal of Machine Learning Research 21, 1-36, 2020 | 363* | 2020 |
| Is Local SGD Better than Minibatch SGD? B Woodworth, KK Patel, SU Stich, Z Dai, B Bullins, HB McMahan, ... ICML 2020 - International Conference on Machine Learning, 2020 | 347 | 2020 |
| Decentralized Deep Learning with Arbitrary Communication Compression A Koloskova, T Lin, SU Stich, M Jaggi ICLR 2020 - International Conference on Learning Representations, 2020 | 306 | 2020 |
| Dynamic Model Pruning with Feedback T Lin, SU Stich, L Barba, D Dmitriev, M Jaggi ICLR 2020 - International Conference on Learning Representations, 2020 | 293 | 2020 |
| ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! K Mishchenko, G Malinovsky, S Stich, P Richtárik ICML 2022 - International Conference on Machine Learning, 2022 | 239 | 2022 |
| Stochastic distributed learning with gradient quantization and double-variance reduction S Horváth, D Kovalev, K Mishchenko, P Richtárik, S Stich Optimization Methods and Software 38 (1), 91-106, 2023 | 222 | 2023 |
| On the Convergence of SGD with Biased Gradients A Ajalloeian, SU Stich ICML 2020 Workshop - Beyond First Order Methods in ML Systems, arXiv …, 2020 | 189* | 2020 |
| Efficiency of the Accelerated Coordinate Descent Method on Structured Optimization Problems Y Nesterov, SU Stich SIAM Journal on Optimization 27 (1), 110-123, 2017 | 183 | 2017 |
| Unified Optimal Analysis of the (Stochastic) Gradient Method SU Stich arXiv preprint arXiv:1907.04232, 2019 | 157 | 2019 |
Martin JaggiEPFLVerified email at epfl.ch
