59 citations · 149 across the 16 of their papers we have counts for
20 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…
LightTune: Lightweight Forward-Only Online Fine-Tuning with Applications to Link Adaptation
Ramy E. Ali, Federico Penna
Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training co…
All Rivers Run to the Sea: Private Learning with Asymmetric Flows
Yue Niu, Ramy E. Ali, Saurav Prakash +1
Data privacy is of great concern in cloud machine-learning service platforms, when sensitive data are exposed to service providers. While private computing environments (e.g., secu…
Comments on CausalEC: A Causally Consistent Data Storage Algorithm Based on Cross-Object Erasure Coding
Ramy E. Ali
Cadambe and Lyu 2021 presents an erasure coding based algorithm called CausalEC that ensures causal consistency based on cross-object erasure coding. This note shows that the algor…
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…
3LegRace: Privacy-Preserving DNN Training over TEEs and GPUs
Yue Niu, Ramy E. Ali, Salman Avestimehr
Leveraging parallel hardware (e.g. GPUs) for deep neural network (DNN) training brings high computing performance. However, it raises data privacy concerns as GPUs lack a trusted e…