A Family of Hybrid Federated and CentralizedLearning Architectures in Machine Learning

in IEEE Transactions on Cognitive Communications and Networking (2022)

Many of the machine learning tasks focus on cen-tralized learning (CL), which requires the transmission of localdatasets from the clients to a parameter server (PS) entailing hugecommunication overhead ... [more ▼]

Many of the machine learning tasks focus on cen-tralized learning (CL), which requires the transmission of localdatasets from the clients to a parameter server (PS) entailing hugecommunication overhead. To overcome this, federated learning(FL) has been a promising tool, wherein the clients send only themodel updates to the PS instead of the whole dataset. However,FL demands powerful computational resources from the clients.Therefore, not all the clients can participate in training if they donot have enough computational resources. To address this issue,we introduce a more practical approach called hybrid federatedand centralized learning (HFCL), wherein only the clients withsufficient resources employ FL, while the remaining ones sendtheir datasets to the PS, which computes the model on behalf ofthem. Then, the model parameters corresponding to all clientsare aggregated at the PS. To improve the efficiency of datasettransmission, we propose two different techniques: increasedcomputation-per-client and sequential data transmission. TheHFCL frameworks outperform FL with up to20%improvementin the learning accuracy when only half of the clients perform FLwhile having50%less communication overhead than CL since allthe clients collaborate on the learning process with their datasets. [less ▲]

Towards the assessment of realistic hybrid precoding in millimeter wave MIMO systems with hardware impairments

in IET Communications (2021), 15(12), 1606-1619

Hybrid processing in millimeter wave (mmWave) communication has been proposed as a solution to reduce the cost and energy consumption by reducing the number of radio-frequency (RF) chains. However, the ... [more ▼]

Hybrid processing in millimeter wave (mmWave) communication has been proposed as a solution to reduce the cost and energy consumption by reducing the number of radio-frequency (RF) chains. However, the impact of the inevitable residual transceiver hardware impairments (RTHIs), including the residual additive transceiver hardware impairments (RATHIs) and the amplified thermal noise (ATN), has not been sufficiently studied in mmWave hybrid processing. In this work, the hybrid precoder and combiner are designed, which include both digital and analog processing by taking into account the RATHIs and the ATN. In particular, a thorough study is provided to shed light on the degradation of the spectral efficiency (SE) of the practical system. The outcomes show the steady degradation of the performance by the ATN across all SNR values, which becomes increasingly critical for higher values of its variance. Furthermore, it is shown that RATHIs result in degradation of the system only in the high SNR regime. Hence, their impact in mmWave system operating at low SNRs might be negligible. Moreover, an increase concerning the number of streams differentiates the impact between the transmit and receive RATHIs with the latter having a more severe effect. [less ▲]