| Federated sparse training: Lottery aware model compression for resource constrained edge S Babakniya, S Kundu, S Prakash, Y Niu, S Avestimehr NeurIPS 2022 Federated Learning Workshop, 2022 | 12 | 2022 |
| SLoRA: Federated parameter efficient fine-tuning of language models S Babakniya, AR Elkordy, YH Ezzeldin, Q Liu, KB Song, M El-Khamy, ... FL@FM-NeurIPS workshop 2023, 2023 | 9 | 2023 |
| Revisiting Sparsity Hunting in Federated Learning: Why does Sparsity Consensus Matter? S Babakniya, S Kundu, S Prakash, Y Niu, S Avestimehr Transactions on Machine Learning Research, 2023 | 7 | 2023 |
| Defending against poisoning backdoor attacks on federated meta-learning CL Chen, S Babakniya, M Paolieri, L Golubchik ACM Transactions on Intelligent Systems and Technology (TIST) 13 (5), 1-25, 2022 | 5 | 2022 |
| Deep-n-Cheap: An automated efficient and extensible search framework for cost-effective deep learning S Dey, S Babakniya, SC Kanala, M Paolieri, L Golubchik, PA Beerel, ... SN Computer Science 2 (4), 265, 2021 | 2 | 2021 |
| A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks S Babakniya, Z Fabian, C He, M Soltanolkotabi, S Avestimehr Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory S Babakniya, Z Fabian, C He, M Soltanolkotabi, S Avestimehr ICML 2023 Federated Learning Workshop, 2023 | | 2023 |
| Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge S Babakniya, S Kundu, S Prakash, Y Niu, S Avestimehr arXiv preprint arXiv:2208.13092, 2022 | | 2022 |
