44 citations · 84 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
GPT-FL: Generative Pre-trained Model-Assisted Federated Learning
Tuo Zhang, Tiantian Feng, Samiul Alam +5
In this work, we propose GPT-FL, a generative pre-trained model-assisted federated learning (FL) framework. At its core, GPT-FL leverages generative pre-trained models to generate…
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training
Tuo Zhang, Lei Gao, Sunwoo Lee +2
In cross-device Federated Learning (FL) environments, scaling synchronous FL methods is challenging as stragglers hinder the training process. Moreover, the availability of each cl…
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction
Samiul Alam, Luyang Liu, Ming Yan +1
Most cross-device federated learning (FL) studies focus on the model-homogeneous setting where the global server model and local client models are identical. However, such constrai…
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities
Tuo Zhang, Lei Gao, Chaoyang He +3
Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. W…