Publications (54)
Detailed comparison of communication efficiency of split learning and federated learning
Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta +1
No Peek: A Survey of private distributed deep learning
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar +2
Decouple-and-Sample: Protecting sensitive information in task agnostic data release
Abhishek Singh, Ethan Garza, Ayush Chopra +3
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So +17
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
Sihun Baek, Jihong Park, Praneeth Vepakomma +3
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning
Yusuke Koda, Jihong Park, Mehdi Bennis +2
A Review of Homomorphic Encryption Libraries for Secure Computation
Sai Sri Sathya, Praneeth Vepakomma, Ramesh Raskar +2
Learning in the Null Space: Small Singular Values for Continual Learning
Cuong Anh Pham, Praneeth Vepakomma, Samuel Horváth
Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning
Kaustubh Ponkshe, Raghav Singhal, Eduard Gorbunov +3
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning
Ayush Chopra, Surya Kant Sahu, Abhishek Singh +4
DAVED: Data Acquisition via Experimental Design for Data Markets
Charles Lu, Baihe Huang, Sai Praneeth Karimireddy +3
Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach
Chaouki Ben Issaid, Praneeth Vepakomma, Mehdi Bennis
Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix
Seungeun Oh, Sihun Baek, Jihong Park +5
DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images
Zaid Tasneem, Akshat Dave, Abhishek Singh +4
DP-Fusion: Token-Level Differentially Private Inference for Large Language Models
Rushil Thareja, Preslav Nakov, Praneeth Vepakomma +1
Effects of Privacy-Inducing Noise on Welfare and Influence of Referendum Systems
Suat Evren, Praneeth Vepakomma
COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers
Ramesh Raskar, Greg Nadeau, John Werner +21
ABBA-Adapters: Efficient and Expressive Fine-Tuning of Foundation Models
Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak +1
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish +3
Private independence testing across two parties
Praneeth Vepakomma, Mohammad Mohammadi Amiri, Clément L. Canonne +2
NoPeek: Information leakage reduction to share activations in distributed deep learning
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta +1
Supervised Dimensionality Reduction via Distance Correlation Maximization
Praneeth Vepakomma, Chetan Tonde, Ahmed Elgammal
Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning
Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak +2
Combinatorics of Distance Covariance: Inclusion-Minimal Maximizers of Quasi-Concave Set Functions for Diverse Variable Selection
Praneeth Vepakomma, Yulia Kempner
Optimal bandwidth estimation for a fast manifold learning algorithm to detect circular structure in high-dimensional data
Susovan Pal, Praneeth Vepakomma
Splintering with distributions: A stochastic decoy scheme for private computation
Praneeth Vepakomma, Julia Balla, Ramesh Raskar
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Assessing Disease Exposure Risk with Location Data: A Proposal for Cryptographic Preservation of Privacy
Alex Berke, Michiel Bakker, Praneeth Vepakomma +2
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric
Saiteja Utpala, Praneeth Vepakomma, Nina Miolane
Combinatorial Privacy: Private Multi-Party Bitstream Grand Sum by Hiding in Birkhoff Polytopes
Praneeth Vepakomma
SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
Zheng Lin, Xuanjie Hu, Yuxin Zhang +6
Differentially Private CutMix for Split Learning with Vision Transformer
Seungeun Oh, Jihong Park, Sihun Baek +5
Private measurement of nonlinear correlations between data hosted across multiple parties
Praneeth Vepakomma, Subha Nawer Pushpita, Ramesh Raskar
Privacy in Deep Learning: A Survey
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma +3
PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic
Priyanka Singh, Abhishek Singh, Gabriel Cojocaru +2
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
Shraman Pal, Mansi Uniyal, Jihong Park +5
PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval
Praneeth Vepakomma, Julia Balla, Ramesh Raskar
Power Mechanism: Private Tabular Representation Release for Model Agnostic Consumption
Praneeth Vepakomma, Kaustubh Ponkshe
Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity
Praneeth Vepakomma, Yulia Kempner, Ramesh Raskar
DISCOMAX: A Proximity-Preserving Distance Correlation Maximization Algorithm
Praneeth Vepakomma, Ahmed Elgammal
Predicting Survival of Hemodialysis Patients using Federated Learning
Abhiram Raju, Praneeth Vepakomma
Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic
Ramesh Raskar, Isabel Schunemann, Rachel Barbar +29
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks
Abhishek Singh, Ayush Chopra, Vivek Sharma +4
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish +1
Modulated learning for private and distributed regression with just a single sample per client device
Praneeth Vepakomma, Amirhossein Reisizadeh, Samuel Horváth +1
Safety Subspaces are Not Linearly Distinct: A Fine-Tuning Case Study
Kaustubh Ponkshe, Shaan Shah, Raghav Singhal +1
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
Zheng Lin, Yuxin Zhang, Zhe Chen +6
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest
Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh +3
Split Learning for collaborative deep learning in healthcare
Maarten G. Poirot, Praneeth Vepakomma, Ken Chang +3
SplitNN-driven Vertical Partitioning
Iker Ceballos, Vivek Sharma, Eduardo Mugica +4
Offline and Online KL-Regularized RLHF under Differential Privacy
Yulian Wu, Rushil Thareja, Praneeth Vepakomma +1
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models
Raghav Singhal, Kaustubh Ponkshe, Praneeth Vepakomma
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish +3
Data Markets to support AI for All: Pricing, Valuation and Governance
Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish +1