6 papers
LoRA Provides Differential Privacy by Design via Random Sketching
Saber Malekmohammadi, Golnoosh Farnadi
Low-rank adaptation of language models has been proposed to reduce the computational and memory overhead of fine-tuning pre-trained language models. LoRA incorporates trainable low…
An Operator Splitting View of Federated Learning
Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu +1
Over the past few years, the federated learning () community has witnessed a proliferation of new algorithms. However, our understating of the theory of…
Semi-Variance Reduction for Fair Federated Learning
Saber Malekmohammadi, Yaoliang Yu
Ensuring fairness in a Federated Learning (FL) system, i.e., a satisfactory performance for all of the participating diverse clients, is an important and challenging problem. There…
Differentially Private Clustered Federated Learning
Saber Malekmohammadi, Afaf Taik, Golnoosh Farnadi
Federated learning (FL), which is a decentralized machine learning (ML) approach, often incorporates differential privacy (DP) to provide rigorous data privacy guarantees. Previous…
Sharpness-Aware Parameter Selection for Machine Unlearning
Saber Malekmohammadi, Hong kyu Lee, Li Xiong
It often happens that some sensitive personal information, such as credit card numbers or passwords, are mistakenly incorporated in the training of machine learning models and need…
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
Saber Malekmohammadi, Yaoliang Yu, Yang Cao
High utility and rigorous data privacy are of the main goals of a federated learning (FL) system, which learns a model from the data distributed among some clients. The latter has…