354 citations · 1.3k across the 59 of their papers we have counts for
14 papers · 1 filter
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…
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
Sara Babakniya, Zalan Fabian, Chaoyang He +2
Deep learning models often suffer from forgetting previously learned information when trained on new data. This problem is exacerbated in federated learning (FL), where the data is…
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
Shanshan Han, Wenxuan Wu, Baturalp Buyukates +4
Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations…
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
Samiul Alam, Tuo Zhang, Tiantian Feng +8
There is a significant relevance of federated learning (FL) in the realm of Artificial Intelligence of Things (AIoT). However, most existing FL works do not use datasets collected…
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…
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization
Yue Niu, Zalan Fabian, Sunwoo Lee +2
Quasi-Newton methods still face significant challenges in training large-scale neural networks due to additional compute costs in the Hessian related computations and instability i…