Search Results for author: Sajal K. Das

Found 8 papers, 1 papers with code

Securing Federated Learning against Overwhelming Collusive Attackers

no code implementations28 Sep 2022 Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das

While relaxing this assumption that anyway does not hold in reality due to the heterogeneous nature of devices, federated learning (FL) has emerged as a privacy-preserving solution to train a collaborative model over non-iid data distributed across a massive number of devices.

Federated Learning Privacy Preserving

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

no code implementations3 Sep 2022 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 energy consumption substantially.

Federated Learning Privacy Preserving

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

no code implementations3 Sep 2022 Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das

In this paper, we propose a federated learning approach to suppress the unequal distribution of the noisy labels in the dataset of each participant.

Federated Learning Privacy Preserving

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

1 code implementation14 Aug 2022 Ashish Gupta, Tie Luo, Mao V. Ngo, Sajal K. Das

Not only this, but we can also distinguish between targeted and untargeted attacks among malicious clients, unlike most prior works which only consider one type of the attacks.

Federated Learning

Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder

no code implementations2 Apr 2022 Yeahia Sarker, Abdullah-Al-Zubaer Imran, Md Hafiz Ahamed, Ripon K. Chakrabortty, Michael J. Ryan, Sajal K. Das

To harvest maximum details for various receptive regions and high-quality synthetic images, \texttt{NLVAE} is introduced as a self-supervised strategy that reconstructs high-resolution images using disentangled information from the non-local neighbourhood.

Image Super-Resolution Single Image Super Resolution

A Comprehensive Investigation on Range-free Localization Algorithms with Mobile Anchors at Different Altitudes

no code implementations10 Feb 2021 Francesco Betti Sorbelli, Sajal K. Das, Cristina M. Pinotti, Giulio Rigoni

For improving the performance, we propose range-based (RB) variants of the compared algorithms in which, instead of using the observed or the manufacturer radii, the actual measured distances between the MA and the GD are used.

Measurement Errors in Range-Based Localization Algorithms for UAVs: Analysis and Experimentation

no code implementations15 Dec 2020 Francesco Betti Sorbelli, Cristina M. Pinotti, Simone Silvestri, Sajal K. Das

We validate our theoretical analysis and compare the observed localization error of the algorithms after collecting data from a testbed using ten GDs and one drone, equipped with ultra wide band (UWB) antennas and operating in an open field.

CSWA: Aggregation-Free Spatial-Temporal Community Sensing

no code implementations15 Nov 2017 Jiang Bian, Haoyi Xiong, Yanjie Fu, Sajal K. Das

In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}.

Compressive Sensing Distributed Optimization

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