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Found 17 papers, 5 papers with code
Faster Federated Learning with Decaying Number of Local SGD Steps
FedAvg can improve the communication-efficiency of training by performing more steps of Stochastic Gradient Descent (SGD) on clients in each round.
Model-Agnostic Reachability Analysis on Deep Neural Networks
Verification plays an essential role in the formal analysis of safety-critical systems.
Federated Ensemble Model-based Reinforcement Learning in Edge Computing
Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices without gathering their data.
Accelerating Federated Learning with a Global Biased Optimiser
To address this challenge, we propose a novel, generalised approach for incorporating adaptive optimisation into FL with the Federated Global Biased Optimiser (FedGBO) algorithm.
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks
Finally, we provide experiments to evaluate the learning performance of HFL and our MACFL.
Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach
To address these challenges, we propose a novel learning-driven method, which is user-centric and can make effective online migration decisions by utilizing incomplete system-level information.
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts.
Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing
MTFL is compatible with popular iterative FL optimisation algorithms such as Federated Averaging (FedAvg), and we show empirically that a distributed form of Adam optimisation (FedAvg-Adam) benefits convergence speed even further when used as the optimisation strategy within MTFL.
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT
The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but public concern over data privacy means users are apprehensive to send data to a central server for machine learning (ML) purposes.
Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning
Resource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads.
Routing-Led Placement of VNFs in Arbitrary Networks
The ever increasing demand for computing resources has led to the creation of hyperscale datacentres with tens of thousands of servers.
Fractional order graph neural network
This paper proposes fractional order graph neural networks (FGNNs), optimized by the approximation strategy to address the challenges of local optimum of classic and fractional graph neural networks which are specialised at aggregating information from the feature and adjacent matrices of connected nodes and their neighbours to solve learning tasks on non-Euclidean data such as graphs.
Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multi-Objective Optimisation Using Reference Points
The ultimate goal of multi-objective optimisation is to help a decision maker (DM) identify solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple conflicting criteria.
A DoA Estimation Based Robust Beam Forming Method for UAV-BS Communication
Then, we propose a DoA estimation algorithm and a steering vector adaptive receiving beam forming method.
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework
The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation.
Towards Experienced Anomaly Detector through Reinforcement Learning
This abstract proposes a time series anomaly detector which 1) makes no assumption about the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of threshold setting for good anomaly detection performance under specific scenarios, and 3) keeps evolving with the growth of anomaly detection experience.
Social Computing for Mobile Big Data in Wireless Networks
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain.
