Browsing by Subject "federated learning"
Now showing items 1-5 of 5
-
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource-Constrained Devices Using Divide and Collaborative Training
(2024-01)In Federated Learning (FL), the size of local models matters. On the one hand, it is logical to use large-capacity neural networks in pursuit of high performance. On the other hand, deep convolutional neural networks (CNNs) ... -
FedDRL: Deep reinforcement learning-based adaptive aggregation for non-IID data in federated learning
(2022-08-04)The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions ... -
A Survey on Deep Learning Advances and Emerging Issues in Pneumonia and COVID19 Prediction
(2022-01-17)As the COVID19 pandemic evolves and coronavirus mutates to different variants, a high workload falls on the shoulders of doctors and radiologists. Identifying COVID19 through X-ray and Computed Tomography (CT) scanning in ... -
Task-oriented communication design in cyber-physical systems: A survey on theory and applications
(2023-05-25)Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era of cyber-physical ... -
Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks
(2022-10-19)Federated learning (FL) has recently received considerable attention and is becoming a popular machine learning (ML) framework that allows clients to train machine learning models in a decentralized fashion without sharing ...
