33 citations · 35 across the 8 of their papers we have counts for
5 papers · 1 filter
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,…