Search Results for author: Sanjeev R. Kulkarni

Found 13 papers, 2 papers with code

Federated Learning with Downlink Device Selection

no code implementations7 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.

Federated Learning Image Classification

Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence

no code implementations26 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.

Blind Federated Edge Learning

no code implementations19 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).

Convergence of Federated Learning over a Noisy Downlink

no code implementations25 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.

Federated Learning Quantization

Federated Learning With Quantized Global Model Updates

no code implementations18 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.

Federated Learning Quantization

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

no code implementations28 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.

Federated Learning

SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection

1 code implementation25 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

TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking

1 code implementation25 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.

Saliency Prediction

Machine Learning Methods for Attack Detection in the Smart Grid

no code implementations22 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.

Fusion of Image Segmentation Algorithms using Consensus Clustering

no code implementations18 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.

Semantic Segmentation Stochastic Optimization

Wisdom of the Crowd: Incorporating Social Influence in Recommendation Models

no code implementations3 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.

Collaborative Filtering Decision Making +1

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