Semi-decentralized federated learning with cooperative D2D local model aggregations
Federated learning has emerged as a popular technique for distributing machine learning
(ML) model training across the wireless edge. In this paper, we propose two timescale …
(ML) model training across the wireless edge. In this paper, we propose two timescale …
Edge network optimization based on ai techniques: A survey
M Pooyandeh, I Sohn - Electronics, 2021 - mdpi.com
The network edge is becoming a new solution for reducing latency and saving bandwidth in
the Internet of Things (IoT) network. The goal of the network edge is to move computation …
the Internet of Things (IoT) network. The goal of the network edge is to move computation …
Are you left out? an efficient and fair federated learning for personalized profiles on wearable devices of inferior networking conditions
Wearable computers engage in percutaneous interactions with human users and
revolutionize the way of learning human activities. Due to rising privacy concerns, federated …
revolutionize the way of learning human activities. Due to rising privacy concerns, federated …
Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G
An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to
accelerate the response of IoT services by deploying edge intelligence near IoT devices …
accelerate the response of IoT services by deploying edge intelligence near IoT devices …
Federated generalized Bayesian learning via distributed stein variational gradient descent
This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-
parametric generalized Bayesian inference framework for federated learning. DSVGD …
parametric generalized Bayesian inference framework for federated learning. DSVGD …
Delay-aware hierarchical federated learning
Federated learning has gained popularity as a means of training models distributed across
the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) …
the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) …
Loss tolerant federated learning
Federated learning has attracted attention in recent years for collaboratively training data on
distributed devices with privacy-preservation. The limited network capacity of mobile and IoT …
distributed devices with privacy-preservation. The limited network capacity of mobile and IoT …
Feature-contrastive graph federated learning: Responsible ai in graph information analysis
Federated learning enables multiple clients to learn a general model without sharing local
data, and the federated learning system also improves information security and advances …
data, and the federated learning system also improves information security and advances …
A composition–decomposition based federated learning
C Sun, X Wang, J Ma, G Xie - Complex & Intelligent Systems, 2024 - Springer
Federated learning has been shown to be efficient for training a global model without
needing to collect all data from multiple entities to the centralized server. However, the …
needing to collect all data from multiple entities to the centralized server. However, the …
Energy-Efficient and Privacy-Preserved Incentive Mechanism for Mobile Edge Computing-Assisted Federated Learning in Healthcare System
Recent advancements in the Internet of Medical Things (IoMT) have significantly influenced
the development of smart healthcare systems. Mobile edge computing (MEC)-assisted …
the development of smart healthcare systems. Mobile edge computing (MEC)-assisted …