33 citations · 35 across the 8 of their papers we have counts for
8 papers
Distributed Optimization with Streaming Data: A Temporal Weighting Perspective
Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson
Optimization theory is a widely used tool for intelligent decision-making. While classical optimization deals with fixed, time-invariant objective functions, many modern applicatio…
Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting
Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson
Classical optimization theory largely focuses on fixed objective functions, whereas many modern learning systems operate in dynamic environments where data arrive sequentially and…
Time-Varying Optimization for Streaming Data Via Temporal Weighting
Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson
Classical optimization theory deals with fixed, time-invariant objective functions. However, time-varying optimization has emerged as an important subject for decision-making in dy…
Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off
Muhammad Faraz Ul Abrar, Nicolò Michelusi
Over-the-air (OTA) federated learning (FL) has been well recognized as a scalable paradigm that exploits the waveform superposition of the wireless multiple-access channel to aggre…
Biased Federated Learning under Wireless Heterogeneity
Muhammad Faraz Ul Abrar, Nicolò Michelusi
Federated learning (FL) has emerged as a promising framework for distributed learning, enabling collaborative model training without sharing private data. Existing wireless FL work…
Biased Over-the-Air Federated Learning under Wireless Heterogeneity
Muhammad Faraz Ul Abrar, Nicolò Michelusi
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…
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach
Muhammad Faraz Ul Abrar, Nicolò Michelusi
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,…
STAR-RIS-Assisted Hybrid NOMA mmWave Communication: Optimization and Performance Analysis
Muhammad Faraz Ul Abrar, Muhammad Talha, Rafay Iqbal Ansari +2
Simultaneously reflecting and transmitting reconfigurable intelligent surfaces (STAR-RIS) has recently emerged as prominent technology that exploits the transmissive property of RI…