5 citations · 5 across the 2 of their papers we have counts for
16 papers
Supercharging Federated Learning with Flower and NVIDIA FLARE
Holger R. Roth, Daniel J. Beutel, Yan Cheng +13
Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicate…
Attacks on Third-Party APIs of Large Language Models
Wanru Zhao, Vidit Khazanchi, Haodi Xing +3
Large language model (LLM) services have recently begun offering a plugin ecosystem to interact with third-party API services. This innovation enhances the capabilities of LLMs, bu…
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
Wanru Zhao, Yaxin Du, Nicholas Donald Lane +2
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research. To further s…
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
Xinchi Qiu, Yan Gao, Lorenzo Sani +6
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized. The deploym…
Federated Learning Priorities Under the European Union Artificial Intelligence Act
Herbert Woisetschläger, Alexander Erben, Bill Marino +4
The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way. Our key inquiry is how this will affect Federated Learning (FL),…
Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads
Hongxiang Fan, Stylianos I. Venieris, Alexandros Kouris +1
Running multiple deep neural networks (DNNs) in parallel has become an emerging workload in both edge devices, such as mobile phones where multiple tasks serve a single user for da…