activity
20222025
most citedLong-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning

3 citations · 7 across the 14 of their papers we have counts for

collaborators

5 papers

cs.GT2024

TASR: A Novel Trust-Aware Stackelberg Routing Algorithm to Mitigate Traffic Congestion

Doris E. M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das

Stackelberg routing platforms (SRP) reduce congestion in one-shot traffic networks by proposing optimal route recommendations to selfish travelers. Traditionally, Stackelberg routi…

cs.DS20232 cited

Dispatching Point Selection for a Drone-Based Delivery System Operating in a Mixed Euclidean-Manhattan Grid

Francesco Betti Sorbelli, Federico Corò, Sajal K. Das +2

In this paper, we present a drone-based delivery system that assumes to deal with two different mixed-areas, i.e., rural and urban. In these mixed-areas, called EM-grids, the dista…

cs.LG20221 cited

Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning

Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta +1

Smart sensing provides an easier and convenient data-driven mechanism for monitoring and control in the built environment. Data generated in the built environment are privacy sensi…

cs.LG20221 cited

FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Buildings

Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das

With the enhancement of people's living standards and rapid growth of communication technologies, residential environments are becoming smart and well-connected, increasing overall…

cs.CR20223 cited

Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning

Ashish Gupta, Tie Luo, Mao V. Ngo +1

Federated learning offers a framework of training a machine learning model in a distributed fashion while preserving privacy of the participants. As the server cannot govern the cl…