Praneeth Vepakomma

Orcid: 0000-0003-2296-9296

According to our database1, Praneeth Vepakomma authored at least 51 papers between 2015 and 2024.

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Bibliography

2024
Mix2SFL: Two-Way Mixup for Scalable, Accurate, and Communication-Efficient Split Federated Learning.
IEEE Trans. Big Data, June, 2024

Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix.
CoRR, 2024

SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models.
CoRR, 2024

Data Acquisition via Experimental Design for Decentralized Data Markets.
CoRR, 2024

DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images.
CoRR, 2024

Effects of Privacy-Inducing Noise on Welfare and Influence of Referendum Systems.
Proceedings of the 5th Symposium on Foundations of Responsible Computing, 2024

2023
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric.
Trans. Mach. Learn. Res., 2023

Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Parallel Quasi-Concave Set Function Optimization for Scalability Even Without Submodularity.
Proceedings of the IEEE High Performance Extreme Computing Conference, 2023

2022
Differentially Private CutMix for Split Learning with Vision Transformer.
CoRR, 2022

Private independence testing across two parties.
CoRR, 2022

Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning.
CoRR, 2022

The Privacy-Welfare Trade-off: Effects of Differential Privacy on Influence & Welfare in Social Choice.
CoRR, 2022

LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning.
Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

An Automated Framework for Distributed Deep Learning-A Tool Demo.
Proceedings of the 42nd IEEE International Conference on Distributed Computing Systems, 2022

Decouple-and-Sample: Protecting Sensitive Information in Task Agnostic Data Release.
Proceedings of the Computer Vision - ECCV 2022, 2022

Blind Inference: An Automated Privacy-Preserving Prediction Service using Secure Multi-Party Computation for Medical Applications.
Proceedings of the AMIA 2022, 2022

PrivateMail: Supervised Manifold Learning of Deep Features with Privacy for Image Retrieval.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Split Learning: A Resource Efficient Model and Data Parallel Approach for Distributed Deep Learning.
Proceedings of the Federated Learning, 2022

2021
Advances and Open Problems in Federated Learning.
Found. Trends Mach. Learn., 2021

Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning.
CoRR, 2021

AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning.
CoRR, 2021

Private measurement of nonlinear correlations between data hosted across multiple parties.
CoRR, 2021

Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity.
CoRR, 2021

Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval.
CoRR, 2021

AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning.
Proceedings of the IEEE Global Communications Conference, 2021

NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training.
Proceedings of the 16th IEEE International Conference on Automatic Face and Gesture Recognition, 2021

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for Deep Neural Networks.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic.
CoRR, 2020

SplitNN-driven Vertical Partitioning.
CoRR, 2020

FedML: A Research Library and Benchmark for Federated Machine Learning.
CoRR, 2020

COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers.
CoRR, 2020

Privacy in Deep Learning: A Survey.
CoRR, 2020

Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic.
CoRR, 2020

Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic.
CoRR, 2020

NoPeek: Information leakage reduction to share activations in distributed deep learning.
Proceedings of the 20th International Conference on Data Mining Workshops, 2020

2019
Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm.
Discret. Appl. Math., 2019

Split Learning for collaborative deep learning in healthcare.
CoRR, 2019

Advances and Open Problems in Federated Learning.
CoRR, 2019

ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations.
CoRR, 2019

ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries.
CoRR, 2019

Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest.
CoRR, 2019

Detailed comparison of communication efficiency of split learning and federated learning.
CoRR, 2019

Data Markets to support AI for All: Pricing, Valuation and Governance.
CoRR, 2019

2018
No Peek: A Survey of private distributed deep learning.
CoRR, 2018

A Review of Homomorphic Encryption Libraries for Secure Computation.
CoRR, 2018

Split learning for health: Distributed deep learning without sharing raw patient data.
CoRR, 2018

2017
A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease.
Comput. Methods Programs Biomed., 2017

2016
Supervised Dimensionality Reduction via Distance Correlation Maximization.
CoRR, 2016

2015
Iterative Embedding with Robust Correction using Feedback of Error Observed.
Proceedings of the 4th Workshop on Machine Learning for Interactive Systems, 2015

A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities.
Proceedings of the 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, 2015


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