34 citations · 44 across the 7 of their papers we have counts for
16 papers
Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction
Ahmed P. Mohamed, Byunghyun Lee, Yaguang Zhang +4
Machine learning (ML) offers a promising solution to pathloss prediction. However, its effectiveness can be degraded by the limited availability of data. To alleviate these challen…
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…
Adversarial Channels with O(1)-Bit Partial Feedback
Eric Ruzomberka, Yongkyu Jang, David J. Love +1
We consider point-to-point communication over -ary adversarial channels with partial noiseless feedback. In this setting, a sender Alice transmits symbols from a -ary alp…
Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning
Junghoon Kim, Taejoon Kim, David Love +1
The design of codes for feedback-enabled communications has been a long-standing open problem. Recent research on non-linear, deep learning-based coding schemes have demonstrated s…
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…