58 citations · 176 across the 35 of their papers we have counts for
10 papers · 1 filter
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Prashant Khanduri, Pranay Sharma, Haibo Yang +4
Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a joint model by using local data. Despite extensive research, for a generic non-convex FL pr…
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing
Geethu Joseph, M. Cenk Gursoy, Pramod K. Varshney
We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes one process at a time and ob…
Anomaly Detection via Controlled Sensing and Deep Active Inference
Geethu Joseph, Chen Zhong, M. Cenk Gursoy +2
In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes. To this end, the decision-making agen…
Decentralized Federated Learning via Mutual Knowledge Transfer
Chengxi Li, Gang Li, Pramod K. Varshney
In this paper, we investigate the problem of decentralized federated learning (DFL) in Internet of things (IoT) systems, where a number of IoT clients train models collectively for…
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning
Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura +3
Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning applications. It is used for solving optimizati…
Anomalous Example Detection in Deep Learning: A Survey
Saikiran Bulusu, Bhavya Kailkhura, Bo Li +2
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detect…