A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities. - Praneeth Vepakomma; Debraj De; Sajal K. Das 001; Shekhar Bhansali (BSN, 2015)

Iterative Embedding with Robust Correction using Feedback of Error Observed. - Praneeth Vepakomma; Ahmed M. Elgammal (MLIS@ICML, 2015)

Supervised Dimensionality Reduction via Distance Correlation Maximization. - Praneeth Vepakomma; Chetan Tonde; Ahmed M. Elgammal (CoRR, 2016)

A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease. - Prashant Shrivastava; Anupam Shukla; Praneeth Vepakomma; Neera Bhansali; Kshitij Verma (Comput. Methods Programs Biomed., 2017)

Split learning for health: Distributed deep learning without sharing raw patient data. - Praneeth Vepakomma; Otkrist Gupta; Tristan Swedish; Ramesh Raskar (CoRR, 2018)

A Review of Homomorphic Encryption Libraries for Secure Computation. - Sai Sri Sathya; Praneeth Vepakomma; Ramesh Raskar; Ranjan Ramachandra; Santanu Bhattacharya (CoRR, 2018)

No Peek: A Survey of private distributed deep learning. - Praneeth Vepakomma; Tristan Swedish; Ramesh Raskar; Otkrist Gupta; Abhimanyu Dubey (CoRR, 2018)

Data Markets to support AI for All: Pricing, Valuation and Governance. - Ramesh Raskar; Praneeth Vepakomma; Tristan Swedish; Aalekh Sharan (CoRR, 2019)

Detailed comparison of communication efficiency of split learning and federated learning. - Abhishek Singh 005; Praneeth Vepakomma; Otkrist Gupta; Ramesh Raskar (CoRR, 2019)

Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest. - Indu Ilanchezian; Praneeth Vepakomma; Abhishek Singh 005; Otkrist Gupta; G. N. Srinivasa Prasanna; Ramesh Raskar (CoRR, 2019)

ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries. - Vivek Sharma 001; Praneeth Vepakomma; Tristan Swedish; Ken Chang; Jayashree Kalpathy-Cramer; Ramesh Raskar (CoRR, 2019)

ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations. - Vivek Sharma 001; Praneeth Vepakomma; Tristan Swedish; Ken Chang; Jayashree Kalpathy-Cramer; Ramesh Raskar (CoRR, 2019)

Advances and Open Problems in Federated Learning. - Peter Kairouz; H. Brendan McMahan; Brendan Avent; Aurélien Bellet; Mehdi Bennis; Arjun Nitin Bhagoji; Kallista A. Bonawitz; Zachary Charles; Graham Cormode; Rachel Cummings; Rafael G. L. D'Oliveira; Salim El Rouayheb; et al. (CoRR, 2019)

Split Learning for collaborative deep learning in healthcare. - Maarten G. Poirot; Praneeth Vepakomma; Ken Chang; Jayashree Kalpathy-Cramer; Rajiv Gupta; Ramesh Raskar (CoRR, 2019)

Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm. - Praneeth Vepakomma; Yulia Kempner (Discret. Appl. Math., 2019)

NoPeek: Information leakage reduction to share activations in distributed deep learning. - Praneeth Vepakomma; Abhishek Singh 005; Otkrist Gupta; Ramesh Raskar (ICDM (Workshops), 2020)

Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic. - Ramesh Raskar; Isabel Schunemann; Rachel Barbar; Kristen Vilcans; Jim Gray; Praneeth Vepakomma; Suraj Kapa; Andrea Nuzzo; Rajiv Gupta; Alex Berke; Dazza Greenwood; Christian Keegan; et al. (CoRR, 2020)

Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic. - Alex Berke; Michiel A. Bakker; Praneeth Vepakomma; Ramesh Raskar; Kent Larson; Alex 'Sandy' Pentland (CoRR, 2020)

Privacy in Deep Learning: A Survey. - Fatemehsadat Mireshghallah; Mohammadkazem Taram; Praneeth Vepakomma; Abhishek Singh 005; Ramesh Raskar; Hadi Esmaeilzadeh (CoRR, 2020)

COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers. - Ramesh Raskar; Greg Nadeau; John Werner; Rachel Barbar; Ashley Mehra; Gabriel Harp; Markus Leopoldseder; Bryan Wilson; Derrick Flakoll; Praneeth Vepakomma; Deepti Pahwa; Robson Beaudry; et al. (CoRR, 2020)

FedML: A Research Library and Benchmark for Federated Machine Learning. - Chaoyang He 001; Songze Li; Jinhyun So; Mi Zhang 002; Hongyi Wang 001; Xiaoyang Wang; Praneeth Vepakomma; Abhishek Singh 005; Hang Qiu 001; Li Shen 005; Peilin Zhao; Yan Kang 001; et al. (CoRR, 2020)

SplitNN-driven Vertical Partitioning. - Iker Ceballos; Vivek Sharma 001; Eduardo Mugica; Abhishek Singh 005; Alberto Roman; Praneeth Vepakomma; Ramesh Raskar (CoRR, 2020)

PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic. - Priyanka Singh; Abhishek Singh 005; Gabriel Cojocaru; Praneeth Vepakomma; Ramesh Raskar (CoRR, 2020)

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for Deep Neural Networks. - Abhishek Singh 005; Ayush Chopra; Ethan Garza; Emily Zhang; Praneeth Vepakomma; Vivek Sharma 001; Ramesh Raskar (CVPR, 2021)

NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training. - Praneeth Vepakomma; Abhishek Singh 005; Emily Zhang; Otkrist Gupta; Ramesh Raskar (FG, 2021)

AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning. - Yusuke Koda; Jihong Park; Mehdi Bennis; Praneeth Vepakomma; Ramesh Raskar (GLOBECOM, 2021)

Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval. - Praneeth Vepakomma; Julia Balla; Ramesh Raskar (CoRR, 2021)

Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity. - Praneeth Vepakomma; Yulia Kempner; Ramesh Raskar (CoRR, 2021)

Private measurement of nonlinear correlations between data hosted across multiple parties. - Praneeth Vepakomma; Subha Nawer Pushpita; Ramesh Raskar (CoRR, 2021)

AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning. - Ayush Chopra; Surya Kant Sahu; Abhishek Singh 005; Abhinav Java; Praneeth Vepakomma; Vivek Sharma 001; Ramesh Raskar (CoRR, 2021)

Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning. - Shraman Pal; Mansi Uniyal; Jihong Park; Praneeth Vepakomma; Ramesh Raskar; Mehdi Bennis; Moongu Jeon; Jinho Choi 001 (CoRR, 2021)

Advances and Open Problems in Federated Learning. - Peter Kairouz; H. Brendan McMahan; Brendan Avent; Aurélien Bellet; Mehdi Bennis; Arjun Nitin Bhagoji; Kallista A. Bonawitz; Zachary Charles; Graham Cormode; Rachel Cummings; Rafael G. L. D'Oliveira; Hubert Eichner; et al. (Found. Trends Mach. Learn., 2021)

Split Learning: A Resource Efficient Model and Data Parallel Approach for Distributed Deep Learning. - Praneeth Vepakomma; Ramesh Raskar (Federated Learning, 2022)

PrivateMail: Supervised Manifold Learning of Deep Features with Privacy for Image Retrieval. - Praneeth Vepakomma; Julia Balla; Ramesh Raskar (AAAI, 2022)

Blind Inference: An Automated Privacy-Preserving Prediction Service using Secure Multi-Party Computation for Medical Applications. - Gharib Gharibi; Babak Poorebrahim Gilkalaye; Praneeth Vepakomma; Zachi Attia; Riddhiman Das; Suraj Kapa; Ramesh Raskar (AMIA, 2022)

Decouple-and-Sample: Protecting Sensitive Information in Task Agnostic Data Release. - Abhishek Singh 005; Ethan Garza; Ayush Chopra; Praneeth Vepakomma; Vivek Sharma 001; Ramesh Raskar (ECCV (13), 2022)

An Automated Framework for Distributed Deep Learning-A Tool Demo. - Gharib Gharibi; Ravi Patel; Anissa Khan; Babak Poorebrahim Gilkalaye; Praneeth Vepakomma; Ramesh Raskar; Steve Penrod; Greg Storm; Riddhiman Das (ICDCS, 2022)

LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning. - Seungeun Oh; Jihong Park; Praneeth Vepakomma; Sihun Baek; Ramesh Raskar; Mehdi Bennis; Seong-Lyun Kim (WWW, 2022)

The Privacy-Welfare Trade-off: Effects of Differential Privacy on Influence & Welfare in Social Choice. - Ibrahim Suat Evren; Praneeth Vepakomma; Ramesh Raskar (CoRR, 2022)

Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning. - Sihun Baek; Jihong Park; Praneeth Vepakomma; Ramesh Raskar; Mehdi Bennis; Seong-Lyun Kim (CoRR, 2022)

Private independence testing across two parties. - Praneeth Vepakomma; Mohammad Mohammadi Amiri; Clément L. Canonne; Ramesh Raskar; Alex Pentland (CoRR, 2022)

Differentially Private CutMix for Split Learning with Vision Transformer. - Seungeun Oh; Jihong Park; Sihun Baek; Hyelin Nam; Praneeth Vepakomma; Ramesh Raskar; Mehdi Bennis; Seong-Lyun Kim (CoRR, 2022)

Parallel Quasi-Concave Set Function Optimization for Scalability Even Without Submodularity. - Praneeth Vepakomma; Yulia Kempner; Rodmy Paredes Alfaro; Ramesh Raskar (HPEC, 2023)

Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release. - Abhishek Singh 005; Praneeth Vepakomma; Vivek Sharma 001; Ramesh Raskar (NeurIPS, 2023)

Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric. - Saiteja Utpala; Praneeth Vepakomma; Nina Miolane (Trans. Mach. Learn. Res., 2023)

DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images. - Zaid Tasneem; Akshat Dave; Abhishek Singh 005; Kushagra Tiwary; Praneeth Vepakomma; Ashok Veeraraghavan; Ramesh Raskar (ECCV (59), 2024)

Effects of Privacy-Inducing Noise on Welfare and Influence of Referendum Systems. - Ibrahim Suat Evren; Praneeth Vepakomma (FORC, 2024)

Data Acquisition via Experimental Design for Data Markets. - Charles Lu 001; Baihe Huang; Sai Praneeth Karimireddy; Praneeth Vepakomma; Michael I. Jordan; Ramesh Raskar (NeurIPS, 2024)

Data Acquisition via Experimental Design for Decentralized Data Markets. - Charles Lu 001; Baihe Huang; Sai Praneeth Karimireddy; Praneeth Vepakomma; Michael I. Jordan; Ramesh Raskar (CoRR, 2024)

SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models. - Zheng Lin; Xuanjie Hu; Yuxin Zhang; Zhe Chen 015; Zihan Fang; Xianhao Chen; Ang Li 005; Praneeth Vepakomma; Yue Gao 001 (CoRR, 2024)

Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models. - Raghav Singhal; Kaustubh Ponkshe; Praneeth Vepakomma (CoRR, 2024)

Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning. - Kaustubh Ponkshe; Raghav Singhal; Eduard Gorbunov; Alexey Tumanov; Samuel Horváth; Praneeth Vepakomma (CoRR, 2024)

Predicting Survival of Hemodialysis Patients using Federated Learning. - Abhiram Raju; Praneeth Vepakomma (CoRR, 2024)

Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix. - Seungeun Oh; Sihun Baek; Jihong Park; Hyelin Nam; Praneeth Vepakomma; Ramesh Raskar; Mehdi Bennis; Seong-Lyun Kim (Trans. Mach. Learn. Res., 2024)

Mix2SFL: Two-Way Mixup for Scalable, Accurate, and Communication-Efficient Split Federated Learning. - Seungeun Oh; Hyelin Nam; Jihong Park; Praneeth Vepakomma; Ramesh Raskar; Mehdi Bennis; Seong-Lyun Kim (IEEE Trans. Big Data, 2024)

FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Large Language Models. - Raghav Singhal; Kaustubh Ponkshe; Praneeth Vepakomma (ACL (1), 2025)

Towards a Unified Framework for Split Learning. - Boris Radovic; Marco Canini; Samuel Horváth; Veljko Pejovic; Praneeth Vepakomma (EuroMLSys, 2025)

Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach. - Chaouki Ben Issaid; Praneeth Vepakomma; Mehdi Bennis (Trans. Mach. Learn. Res., 2025)

Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning. - Raghav Singhal; Kaustubh Ponkshe; Rohit Vartak; Lav R. Varshney; Praneeth Vepakomma (CoRR, 2025)

HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models. - Zheng Lin; Yuxin Zhang; Zhe Chen 015; Zihan Fang; Xianhao Chen; Praneeth Vepakomma; Wei Ni 001; Jun Luo 001; Yue Gao 001 (CoRR, 2025)

Safety Subspaces are Not Distinct: A Fine-Tuning Case Study. - Kaustubh Ponkshe; Shaan Shah; Raghav Singhal; Praneeth Vepakomma (CoRR, 2025)

ABBA: Highly Expressive Hadamard Product Adaptation for Large Language Models. - Raghav Singhal; Kaustubh Ponkshe; Rohit Vartak; Praneeth Vepakomma (CoRR, 2025)

Convergence of Mean Shift Algorithms for Large Bandwidths and Simultaneous Accurate Clustering. - Susovan Pal; Praneeth Vepakomma (CoRR, 2025)

DP-Fusion: Token-Level Differentially Private Inference for Large Language Models. - Rushil Thareja; Preslav Nakov; Praneeth Vepakomma; Nils Lukas (CoRR, 2025)