5 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…
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…
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…
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…