17 citations · 37 across the 9 of their papers we have counts for
9 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…
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
Yavuz Faruk Bakman, Duygu Nur Yaldiz, Yahya H. Ezzeldin +1
Federated Learning (FL) has gained significant attraction due to its ability to enable privacy-preserving training over decentralized data. Current literature in FL mostly focuses…
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…
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation
Joshua C. Zhao, Atul Sharma, Ahmed Roushdy Elkordy +3
Federated learning was introduced to enable machine learning over large decentralized datasets while promising privacy by eliminating the need for data sharing. Despite this, prior…
Federated Analytics: A survey
Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Shanshan Han +4
Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g.,…
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?
Ahmed Roushdy Elkordy, Jiang Zhang, Yahya H. Ezzeldin +2
Federated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device.…
FairFed: Enabling Group Fairness in Federated Learning
Yahya H. Ezzeldin, Shen Yan, Chaoyang He +2
Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as…
Quantizing data for distributed learning
Osama A. Hanna, Yahya H. Ezzeldin, Christina Fragouli +1
We consider machine learning applications that train a model by leveraging data distributed over a trusted network, where communication constraints can create a performance bottlen…