no code implementations • 15 Dec 2023 • Feng Lu, Wei Li, Yifei Sun, Cheng Song, Yufei Ren, Albert Y. Zomaya
Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time.
no code implementations • 26 Oct 2022 • Zhengjie Yang, Sen Fu, Wei Bao, Dong Yuan, Albert Y. Zomaya
In this paper, we propose Hierarchical Federated Learning with Momentum Acceleration (HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies momentum for training acceleration.
1 code implementation • 14 Oct 2021 • Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Albert Y. Zomaya
COVID-19 has spread rapidly across the globe and become a deadly pandemic.
no code implementations • 10 Feb 2021 • Tiansheng Huang, Weiwei Lin, Xiaobin Hong, Xiumin Wang, Qingbo Wu, Rui Li, Ching-Hsien Hsu, Albert Y. Zomaya
With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery.
2 code implementations • 10 Dec 2020 • Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen, Wei Bao, Amir Rezaei Balef, Bing B. Zhou, Albert Y. Zomaya
In this work, we propose DONE, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning.
no code implementations • 17 Nov 2020 • Tiansheng Huang, Weiwei Lin, Li Shen, Keqin Li, Albert Y. Zomaya
Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public.
no code implementations • 3 Nov 2020 • Tiansheng Huang, Weiwei Lin, Wentai Wu, Ligang He, Keqin Li, Albert Y. Zomaya
The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.
no code implementations • 18 Sep 2020 • Zhengjie Yang, Wei Bao, Dong Yuan, Nguyen H. Tran, Albert Y. Zomaya
It is well-known that Nesterov Accelerated Gradient (NAG) is a more advantageous form of momentum, but it is not clear how to quantify the benefits of NAG in FL so far.
1 code implementation • 5 Aug 2020 • Jin Wang, Jia Hu, Geyong Min, Albert Y. Zomaya, Nektarios Georgalas
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.
no code implementations • 1 Apr 2020 • Zheyi Chen, Jia Hu, Geyong Min, Albert Y. Zomaya, Tarek El-Ghazawi
Resource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads.
4 code implementations • 29 Oct 2019 • Canh T. Dinh, Nguyen H. Tran, Minh N. H. Nguyen, Choong Seon Hong, Wei Bao, Albert Y. Zomaya, Vincent Gramoli
There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data.
no code implementations • 11 Oct 2019 • Bin Qian, Jie Su, Zhenyu Wen, Devki Nandan Jha, Yinhao Li, Yu Guan, Deepak Puthal, Philip James, Renyu Yang, Albert Y. Zomaya, Omer Rana, Lizhe Wang, Maciej Koutny, Rajiv Ranjan
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services.
no code implementations • 19 Apr 2016 • Binqi Zhang, Chen Wang, Bing Bing Zhou, Albert Y. Zomaya
To improve the temporal and spatial storage efficiency, researchers have intensively studied various techniques, including compression and deduplication.