Muhammad Faraz Ul Abrar
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View article: Time-Varying Optimization for Streaming Data Via Temporal Weighting
Time-Varying Optimization for Streaming Data Via Temporal Weighting Open
Classical optimization theory deals with fixed, time-invariant objective functions. However, time-varying optimization has emerged as an important subject for decision-making in dynamic environments. In this work, we study the problem of l…
View article: Biased Over-the-Air Federated Learning under Wireless Heterogeneity
Biased Over-the-Air Federated Learning under Wireless Heterogeneity Open
Recently, Over-the-Air (OTA) computation has emerged as a promising federated learning (FL) paradigm that leverages the waveform superposition properties of the wireless channel to realize fast model updates. Prior work focused on the OTA …
View article: Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach Open
Over-the-air (OTA) computation has recently emerged as a communication-efficient Federated Learning (FL) paradigm to train machine learning models over wireless networks. However, its performance is limited by the device with the worst SNR…
View article: STAR-RIS-Assisted Hybrid NOMA mmWave Communication: Optimization and Performance Analysis
STAR-RIS-Assisted Hybrid NOMA mmWave Communication: Optimization and Performance Analysis Open
Simultaneously reflecting and transmitting reconfigurable intelligent surfaces (STAR-RIS) has recently emerged as prominent technology that exploits the transmissive property of RIS to mitigate the half-space coverage limitation of convent…