no code implementations • 25 Mar 2024 • Nicolo Dal Fabbro, Arman Adibi, H. Vincent Poor, Sanjeev R. Kulkarni, Aritra Mitra, George J. Pappas
We consider a setting in which $N$ agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server.
1 code implementation • 1 Jun 2023 • Zhixu Tao, Kun Yang, Sanjeev R. Kulkarni
This paper focuses on the problem of adversarial attacks from Byzantine machines in a Federated Learning setting where non-Byzantine machines can be partitioned into disjoint clusters.
no code implementations • 7 Jul 2021 • Mohammad Mohammadi Amiri, Sanjeev R. Kulkarni, H. Vincent Poor
At each iteration, the PS broadcasts different quantized global model updates to different participating devices based on the last global model estimates available at the devices.
no code implementations • 17 Jan 2021 • Peng Gao, Fei Shao, Xiaoyuan Liu, Xusheng Xiao, Haoyuan Liu, Zheng Qin, Fengyuan Xu, Prateek Mittal, Sanjeev R. Kulkarni, Dawn Song
Log-based cyber threat hunting has emerged as an important solution to counter sophisticated cyber attacks.
1 code implementation • 26 Oct 2020 • Peng Gao, Fei Shao, Xiaoyuan Liu, Xusheng Xiao, Zheng Qin, Fengyuan Xu, Prateek Mittal, Sanjeev R. Kulkarni, Dawn Song
Log-based cyber threat hunting has emerged as an important solution to counter sophisticated attacks.
no code implementations • 19 Oct 2020 • Mohammad Mohammadi Amiri, Tolga M. Duman, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor
At each iteration, wireless devices perform local updates using their local data and the most recent global model received from the PS, and send their local updates to the PS over a wireless fading multiple access channel (MAC).
no code implementations • 25 Aug 2020 • Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor
The PS has access to the global model and shares it with the devices for local training, and the devices return the result of their local updates to the PS to update the global model.
no code implementations • 18 Jun 2020 • Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor
We analyze the convergence behavior of the proposed LFL algorithm assuming the availability of accurate local model updates at the server.
no code implementations • 28 Jan 2020 • Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor
At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity.
1 code implementation • 25 Jun 2018 • Peng Gao, Xusheng Xiao, Ding Li, Zhichun Li, Kangkook Jee, Zhen-Yu Wu, Chung Hwan Kim, Sanjeev R. Kulkarni, Prateek Mittal
To facilitate the task of expressing anomalies based on expert knowledge, our system provides a domain-specific query language, SAQL, which allows analysts to express models for (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-based anomalies, and (4) outlier-based anomalies.
Cryptography and Security Databases
1 code implementation • 25 Apr 2015 • Pingmei Xu, Krista A. Ehinger, yinda zhang, Adam Finkelstein, Sanjeev R. Kulkarni, Jianxiong Xiao
Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow.
no code implementations • 22 Mar 2015 • Mete Ozay, Inaki Esnaola, Fatos T. Yarman Vural, Sanjeev R. Kulkarni, H. Vincent Poor
The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods.
no code implementations • 18 Feb 2015 • Mete Ozay, Fatos T. Yarman Vural, Sanjeev R. Kulkarni, H. Vincent Poor
A new segmentation fusion method is proposed that ensembles the output of several segmentation algorithms applied on a remotely sensed image.
no code implementations • 3 Aug 2012 • Shang Shang, Sanjeev R. Kulkarni, Paul W. Cuff, Pan Hui
Collaborative filtering (CF) is one of the most popular approaches to build a recommendation system.
no code implementations • 3 Aug 2012 • Shang Shang, Pan Hui, Sanjeev R. Kulkarni, Paul W. Cuff
In this paper, we propose two recommendation models, for individuals and for groups respectively, based on social contagion and social influence network theory.