no code implementations • 12 Aug 2023 • Sara Babakniya, Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Qingfeng Liu, Kee-Bong Song, Mostafa El-Khamy, Salman Avestimehr
In the absence of centralized data, Federated Learning (FL) can benefit from distributed and private data of the FL edge clients for fine-tuning.
no code implementations • 2 Jul 2023 • Sara Babakniya, Zalan Fabian, Chaoyang He, Mahdi Soltanolkotabi, Salman Avestimehr
Deep learning models are prone to forgetting information learned in the past when trained on new data.
no code implementations • ACM Transactions on Intelligent Systems and Technology 2022 2022 • Chien-Lun Chen, Sara Babakniya, Marco Paolieri, Leana Golubchik
Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy.
1 code implementation • 27 Aug 2022 • Sara Babakniya, Souvik Kundu, Saurav Prakash, Yue Niu, Salman Avestimehr
A possible solution to this problem is to utilize off-the-shelf sparse learning algorithms at the clients to meet their resource budget.