Mobile reconfigurable intelligent surfaces for NOMA networks: Federated learning approaches
A novel framework of reconfigurable intelligent surfaces (RISs)-enhanced indoor wireless
networks is proposed, where an RIS mounted on the robot is invoked to enable mobility of …
networks is proposed, where an RIS mounted on the robot is invoked to enable mobility of …
[HTML][HTML] Large-scale water quality prediction using federated sensing and learning: A case study with real-world sensing big-data
Green tide, which is a serious water pollution problem, is caused by the complex
relationships of various factors, such as flow rate, several water quality indicators, and …
relationships of various factors, such as flow rate, several water quality indicators, and …
Federated learning assisted deep q-learning for joint task offloading and fronthaul segment routing in open ran
A Ndikumana, KK Nguyen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Offloading computation-intensive tasks to edge clouds has become an efficient way to
support resource constraint edge devices. However, task offloading delay is an issue largely …
support resource constraint edge devices. However, task offloading delay is an issue largely …
Communication efficient decentralized learning over bipartite graphs
In this paper, we propose a communication-efficiently decentralized machine learning
framework that solves a consensus optimization problem defined over a network of inter …
framework that solves a consensus optimization problem defined over a network of inter …
Multi-agent deep reinforcement learning using attentive graph neural architectures for real-time strategy games
In real-time strategy (RTS) game artificial intelligence research, various multi-agent deep
reinforcement learning (MADRL) algorithms are widely and actively used nowadays. Most of …
reinforcement learning (MADRL) algorithms are widely and actively used nowadays. Most of …
Communication efficient distributed learning with censored, quantized, and generalized group ADMM
In this paper, we propose a communication-efficiently decentralized machine learning
framework that solves a consensus optimization problem defined over a network of inter …
framework that solves a consensus optimization problem defined over a network of inter …
[HTML][HTML] Understanding global aggregation and optimization of federated learning
We investigate the hypothesis that exploring Federated Learning (FL) aggregation methods
can enhance training processes within FL frameworks, particularly in resource-constrained …
can enhance training processes within FL frameworks, particularly in resource-constrained …
Distributed generative adversarial networks for mmWave channel modeling in wireless UAV networks
In this paper, a novel framework is proposed to enable air-to-ground channel modeling over
millimeter wave (mmWave) frequencies in an unmanned aerial vehicle (UAV) wireless …
millimeter wave (mmWave) frequencies in an unmanned aerial vehicle (UAV) wireless …
Age-optimal power allocation in industrial IoT: A risk-sensitive federated learning approach
YL Hsu, CF Liu, S Samarakoon… - 2021 IEEE 32nd …, 2021 - ieeexplore.ieee.org
This work studies a real-time environment monitoring scenario in the industrial Internet of
things, where wireless sensors proactively collect environmental data and transmit it to the …
things, where wireless sensors proactively collect environmental data and transmit it to the …
Adaptive subcarrier, parameter, and power allocation for partitioned edge learning over broadband channels
In this paper, we consider partitioned edge learning (PARTEL), which implements parameter-
server training, a well known distributed learning method, in a wireless network. Thereby …
server training, a well known distributed learning method, in a wireless network. Thereby …