127 citations · 356 across the 54 of their papers we have counts for
63 papers
Budget-Constrained Multi-Consensus Decentralized Gradient Descent
Shuyi Ren, Nicol`o Michelusi, Erik G. Larsson
We investigate decentralized gradient descent (DGD) with emphasis on efficient communication and computation resource utilization under budget constraints. As a first step toward t…
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…
Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling
Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone +2
To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) exploits waveform superposition…
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 Gradient Descent: Bottleneck Regimes and Budget Complexity
Nicolò Michelusi
Decentralized gradient descent (DGD) is widely used for solving distributed optimization problems over networks of agents. While its convergence properties are well understood, les…
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…
Interference-Robust Non-Coherent Over-the-Air Computation for Decentralized Optimization
Nicolò Michelusi
Non-coherent over-the-air (NCOTA) computation enables low-latency and bandwidth-efficient decentralized optimization by exploiting the average energy superposition property of wire…
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…
Joint UAV Placement and Transceiver Design in Multi-User Wireless Relay Networks
Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love +1
In this paper, a novel approach is proposed to improve the minimum signal-to-interference-plus-noise-ratio (SINR) among users in non-orthogonal multi-user wireless relay networks,…
Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Framework
Erik G. Larsson, Nicolo Michelusi
The decentralized gradient descent (DGD) algorithm, and its sibling, diffusion, are workhorses in decentralized machine learning, distributed inference and estimation, and multi-ag…
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…
Hybrid Fingerprint-based Positioning in Cell-Free Massive MIMO Systems
Manish Kumar, Tzu-Hsuan Chou, Byunghyun Lee +3
Recently, there has been an increasing interest in 6G technology for integrated sensing and communications, where positioning stands out as a key application. In the realm of 6G, c…
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection
Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone +1
Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal a…
Orchestrating UAVs for Prioritized Data Harvesting: A Cross-Layer Optimization Perspective
Bharath Keshavamurthy, Nicolo Michelusi
This work describes the orchestration of a fleet of rotary-wing Unmanned Aerial Vehicles (UAVs) for harvesting prioritized traffic from random distributions of heterogeneous users…
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)…
Propagation Measurements and Analyses at 28 GHz via an Autonomous Beam-Steering Platform
Bharath Keshavamurthy, Yaguang Zhang, Christopher R. Anderson +3
This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28…
Non-Coherent Over-the-Air Decentralized Gradient Descent
Nicolo' Michelusi
Implementing Decentralized Gradient Descent (DGD) in wireless systems is challenging due to noise, fading, and limited bandwidth, necessitating topology awareness, transmission sch…
Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs
Bharath Keshavamurthy, Nicolo Michelusi
We describe the orchestration of a decentralized swarm of rotary-wing UAV-relays, augmenting the coverage and service capabilities of a terrestrial base station. Our goal is to min…
Decentralized Federated Learning via Non-Coherent Over-the-Air Consensus
Nicolò Michelusi
This paper presents NCOTA-DGD, a Decentralized Gradient Descent (DGD) algorithm that combines local gradient descent with a novel Non-Coherent Over-The-Air (NCOTA) consensus scheme…
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…
Compressed Training for Dual-Wideband Time-Varying Sub-Terahertz Massive MIMO
Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love +1
6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz) bands to meet the ever-increasing demand for wireless access. Sub-THz communication comes with many existi…