127 citations · 356 across the 54 of their papers we have counts for
12 papers · 1 filter
Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
Ming Xiang, Nicolò Michelusi, Yonina C. Eldar +1
Over-the-air computation has emerged as a scalable and efficient solution for deploying federated learning algorithms in wireless networks by exploiting waveform superposition for…
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,…
Delay-Aware Hierarchical Federated Learning
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Nicolò Michelusi +1
Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL)…
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks
Seyyedali Hosseinalipour, Su Wang, Nicolo Michelusi +4
Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices, via iterative local updates (at devices) and global agg…
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2
Federated learning has emerged as a popular technique for distributing model training across the network edge. Its learning architecture is conventionally a star topology between t…
Learning and Adaptation for Millimeter-Wave Beam Tracking and Training: a Dual Timescale Variational Framework
Muddassar Hussain, Nicolo Michelusi
Millimeter-wave vehicular networks incur enormous beam-training overhead to enable narrow-beam communications. This paper proposes a learning and adaptation framework in which the…
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2
Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid…
Federated Learning with Communication Delay in Edge Networks
Frank Po-Chen Lin, Christopher G. Brinton, Nicolò Michelusi
Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks. This work addresses an im…