354 citations · 833 across the 13 of their papers we have counts for
13 papers
FedPA-LoRA: Product-Aligned Framework for Mitigating Aggregation and Initialization Errors in Heterogeneous Federated LoRA
Juseok Jeon, Ramy E. Ali, Doyun Kwon +3
Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of large language models, but its factorized parameterization creates a tension between accurate aggregation of l…
Universal Auto-encoder Framework for MIMO CSI Feedback
Jinhyun So, Hyukjoon Kwon
Existing auto-encoder (AE)-based channel state information (CSI) frameworks have focused on a specific configuration of user equipment (UE) and base station (BS), and thus the inpu…
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations
Jinhyun So, Kevin Hsieh, Behnaz Arzani +3
Large-scale deployments of low Earth orbit (LEO) satellites collect massive amount of Earth imageries and sensor data, which can empower machine learning (ML) to address global cha…
Secure Aggregation for Buffered Asynchronous Federated Learning
Jinhyun So, Ramy E. Ali, Başak Güler +1
Federated learning (FL) typically relies on synchronous training, which is slow due to stragglers. While asynchronous training handles stragglers efficiently, it does not ensure pr…
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning
Jinhyun So, Chaoyang He, Chien-Sheng Yang +5
Secure model aggregation is a key component of federated learning (FL) that aims at protecting the privacy of each user's individual model while allowing for their global aggregati…
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
Jinhyun So, Ramy E. Ali, Basak Guler +2
Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conv…
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
Jinhyun So, Basak Guler, A. Salman Avestimehr
We consider a collaborative learning scenario in which multiple data-owners wish to jointly train a logistic regression model, while keeping their individual datasets private from…
On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks
Ramy E. Ali, Jinhyun So, A. Salman Avestimehr
Outsourcing deep neural networks (DNNs) inference tasks to an untrusted cloud raises data privacy and integrity concerns. While there are many techniques to ensure privacy and inte…
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So +17
Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsist…
Byzantine-Resilient Secure Federated Learning
Jinhyun So, Basak Guler, A. Salman Avestimehr
Secure federated learning is a privacy-preserving framework to improve machine learning models by training over large volumes of data collected by mobile users. This is achieved th…
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Jinhyun So, Basak Guler, A. Salman Avestimehr
Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A maj…
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
Jinhyun So, Basak Guler, A. Salman Avestimehr
How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML ke…
Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy
Qian Yu, Songze Li, Netanel Raviv +3
We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms. We propo…