A Trust and Energy-Aware Double Deep Reinforcement Learning Scheduling Strategy for Federated Learning on IoT Devices

[1]  Gaith Rjoub,et al.  Formalizing Group and Propagated Trust in Multi-Agent Systems , 2020, IJCAI.

[2]  Srikanth Kandula,et al.  Resource Management with Deep Reinforcement Learning , 2016, HotNets.

[3]  Jing Zeng,et al.  Q-learning based dynamic task scheduling for energy-efficient cloud computing , 2020, Future Gener. Comput. Syst..

[4]  Gaith Rjoub,et al.  BigTrustScheduling: Trust-aware big data task scheduling approach in cloud computing environments , 2020, Future Gener. Comput. Syst..

[5]  Der-Jiunn Deng,et al.  Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network , 2019, IEEE Transactions on Industrial Informatics.

[6]  Ying Chen,et al.  Revenue-optimal task scheduling and resource management for IoT batch jobs in mobile edge computing , 2020, Peer-to-Peer Networking and Applications.

[7]  Shu Luo,et al.  Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning , 2020, Appl. Soft Comput..

[8]  David Silver,et al.  Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.

[9]  Kaibin Huang,et al.  Towards an Intelligent Edge: Wireless Communication Meets Machine Learning , 2018, ArXiv.

[10]  H. Vincent Poor,et al.  Performance Optimization of Federated Learning over Wireless Networks , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[11]  Xu Chen,et al.  In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning , 2018, IEEE Network.

[12]  Gaith Rjoub,et al.  Deep Smart Scheduling: A Deep Learning Approach for Automated Big Data Scheduling Over the Cloud , 2019, 2019 7th International Conference on Future Internet of Things and Cloud (FiCloud).

[13]  Hongjun Dai,et al.  A scheduling algorithm for autonomous driving tasks on mobile edge computing servers , 2019, J. Syst. Archit..

[14]  Dusit Niyato,et al.  Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach , 2019, 2020 IEEE 6th World Forum on Internet of Things (WF-IoT).

[15]  Shuai Yu,et al.  CEFL: Online Admission Control, Data Scheduling, and Accuracy Tuning for Cost-Efficient Federated Learning Across Edge Nodes , 2020, IEEE Internet of Things Journal.

[16]  Gaith Rjoub,et al.  An endorsement-based trust bootstrapping approach for newcomer cloud services , 2020, Inf. Sci..

[17]  Jamal Bentahar,et al.  Resource-Aware Detection and Defense System against Multi-Type Attacks in the Cloud: Repeated Bayesian Stackelberg Game , 2019, IEEE Transactions on Dependable and Secure Computing.

[18]  Jamal Bentahar,et al.  Monetizing Personal Data: A Two-Sided Market Approach , 2016, ANT/SEIT.

[19]  Gaith Rjoub,et al.  Deep and reinforcement learning for automated task scheduling in large‐scale cloud computing systems , 2020, Concurr. Comput. Pract. Exp..

[20]  Jingyan Jiang,et al.  Decentralized Federated Learning: A Segmented Gossip Approach , 2019, ArXiv.

[21]  Ahmed Saleh Bataineh,et al.  Toward monetizing personal data: A two-sided market analysis , 2020, Future Gener. Comput. Syst..

[22]  Yue Tan,et al.  Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges , 2019, IEEE Communications Surveys & Tutorials.

[23]  Takayuki Nishio,et al.  Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge , 2018, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[24]  Dong In Kim,et al.  Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach , 2018, IEEE Wireless Communications Letters.

[25]  H. Vincent Poor,et al.  Scheduling Policies for Federated Learning in Wireless Networks , 2019, IEEE Transactions on Communications.