9 citations · 9 across the 2 of their papers we have counts for
3 papers
Differentially Private Federated Learning without Noise Addition: When is it Possible?
Jiang Zhang, Konstantinos Psounis, Salman Avestimehr
Federated Learning (FL) with Secure Aggregation (SA) has gained significant attention as a privacy preserving framework for training machine learning models while preventing the se…
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
Sara Babakniya, Ahmed Roushdy Elkordy, Yahya H. Ezzeldin +4
Transfer learning via fine-tuning pre-trained transformer models has gained significant success in delivering state-of-the-art results across various NLP tasks. In the absence of c…
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
Joshua C. Zhao, Ahmed Roushdy Elkordy, Atul Sharma +3
Secure aggregation promises a heightened level of privacy in federated learning, maintaining that a server only has access to a decrypted aggregate update. Within this setting, lin…