127 citations · 461 across the 65 of their papers we have counts for
18 papers · 1 filter
Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading
Dong-Jun Han, Seyyedali Hosseinalipour, David J. Love +2
While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting a…
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks
Su Wang, Roberto Morabito, Seyyedali Hosseinalipour +2
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated b…
Constant Modulus Waveform Design with Block-Level Interference Exploitation for DFRC Systems
Byunghyun Lee, Anindya Bijoy Das, David J. Love +2
Dual-functional radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware. In this paper, we propose an…
Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Existing approaches to distributed matrix computations involve allocating coded combinations of submatrices to worker nodes, to build resilience to stragglers and/or enhance privac…
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning
Satyavrat Wagle, Anindya Bijoy Das, David J. Love +1
Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve convergence speed and reduce model bias through rapid local information exchan…
Derandomizing Codes for the Binary Adversarial Wiretap Channel of Type II
Eric Ruzomberka, Homa Nikbakht, Christopher G. Brinton +2
We revisit the binary adversarial wiretap channel (AWTC) of type II in which an active adversary can read a fraction and flip a fraction of codeword bits. The semantic-secr…
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers
Su Wang, Rajeev Sahay, Adam Piaseczny +1
Recent interest in leveraging federated learning (FL) for radio signal classification (SC) tasks has shown promise but FL-based SC remains susceptible to model poisoning adversaria…
Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization
Zhan-Lun Chang, Seyyedali Hosseinalipour, Mung Chiang +1
Federated learning (FL) has emerged as a key technique for distributed machine learning (ML). Most literature on FL has focused on ML model training for (i) a single task/model, wi…
Dynamic and Robust Sensor Selection Strategies for Wireless Positioning with TOA/RSS Measurement
Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim +3
Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection strategies for 3D wireless…
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation
Su Wang, Seyyedali Hosseinalipour, Christopher G. Brinton
Heterogeneity across devices in federated learning (FL) typically refers to statistical (e.g., non-i.i.d. data distributions) and resource (e.g., communication bandwidth) dimension…
Distributed Linear Solvers and Data Heterogeneity
Boris Velasevic, Rohit Parasnis, Christopher G. Brinton +1
We consider the problem of solving a large-scale system of linear equations in a distributed/federated setting. The taskmaster solves the system with the help of a set of machines,…
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks
Su Wang, Seyyedali Hosseinalipour, Vaneet Aggarwal +4
Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely ne…
Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networks
Rohit Parasnis, Seyyedali Hosseinalipour, Yun-Wei Chu +2
Semi-decentralized federated learning blends the conventional device to-server (D2S) interaction structure of federated model training with localized device-to-device (D2D) communi…
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible…
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?
Su Wang, Rajeev Sahay, Christopher G. Brinton
There has been recent interest in leveraging federated learning (FL) for radio signal classification tasks. In FL, model parameters are periodically communicated from participating…
Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach
Rajeev Sahay, Serena Nicoll, Minjun Zhang +4
We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured in…
Distributed Matrix Computations with Low-weight Encodings
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Straggler nodes are well-known bottlenecks of distributed matrix computations which induce reductions in computation/communication speeds. A common strategy for mitigating such str…
A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO
Myeung Suk Oh, Anindya Bijoy Das, Seyyedali Hosseinalipour +3
Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios. Recently, open-RAN (O-RAN) techniques have begun addin…