| Flower: A friendly federated learning research framework DJ Beutel, T Topal, A Mathur, X Qiu, J Fernandez-Marques, Y Gao, L Sani, ... arXiv preprint arXiv:2007.14390, 2020 | 716 | 2020 |
| Worldwide Federated Training of Language Models A Iacob, L Sani, B Marino, P Aleksandrov, ND Lane arXiv preprint arXiv:2405.14446, 2024 | 1 | 2024 |
| The Future of Large Language Model Pre-training is Federated L Sani, A Iacob, Z Cao, B Marino, Y Gao, T Paulik, W Zhao, WF Shen, ... arXiv preprint arXiv:2405.10853, 2024 | 1 | 2024 |
| Sheaf HyperNetworks for Personalized Federated Learning B Nguyen, L Sani, X Qiu, P Liò, ND Lane arXiv preprint arXiv:2405.20882, 2024 | | 2024 |
| FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients X Qiu, Y Gao, L Sani, H Pan, W Zhao, PPB Gusmao, M Alibeigi, A Iacob, ... arXiv preprint arXiv:2402.10191, 2024 | | 2024 |
| High-throughput Simulation of Federated Learning via Resource-Aware Client Placement L Sani, PPB de Gusmão, A Iacob, W Zhao, X Qiu, Y Gao, ... arXiv preprint arXiv:2306.17453, 2023 | | 2023 |
| WORLDWIDE EDGE-SILO FEDERATED LEARNING OF LANGUAGE MODELS A Iacob, L Sani, B Marino, P Aleksandrov, WF Shen, ND Lane | | |
| SparseFedPP: sparse federated learning for hardware-constrained edge-devices A Guastella, A Mora, P Bellavista, L Sani, AA Iacob, ND Lane | | |
