G Cleland, D Wu, R Ullah… - 2022 IEEE/ACM 15th …, 2022 - ieeexplore.ieee.org
Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. This …
Y Xu, Y Liao, L Wang, H Xu, Z Jiang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated learning (FL) enables edge devices to cooperatively train models without exposing their raw data. However, implementing a practical FL system at the network edge …
Z Yang, S Fu, W Bao, D Yuan… - 2023 IEEE 43rd …, 2023 - ieeexplore.ieee.org
In this paper, we propose and analyze HierAdMo, a three-tier adaptive momentum accelerated client-edge-cloud Federated Learning (FL) algorithm. HierAdMo combines the …
Most Federated Learning (FL) algorithms proposed to date obtain the global model by aggregating multiple local models that typically share the same architecture, thus …
Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration …
Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or …
YG Kim, CJ Wu - MICRO-54: 54th Annual IEEE/ACM International …, 2021 - dl.acm.org
Federated learning enables a cluster of decentralized mobile devices at the edge to collaboratively train a shared machine learning model, while keeping all the raw training …
PK Quan, M Kundroo, T Kim - IEEE Access, 2023 - ieeexplore.ieee.org
Federated learning (FL) is a machine learning system that allows a network of devices to train a model without centralized data. This characteristic makes FL an ideal choice for …
J Yoon, G Park, W Jeong… - … Conference on Machine …, 2022 - proceedings.mlr.press
In practical federated learning scenarios, the participating devices may have different bitwidths for computation and memory storage by design. However, despite the progress …