Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks
-
CDPG
-
January 9, 2025
-
Data Privacy
-
0 Comments
Aug 2024 Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Authors: Pranjal Naman and Yogesh Simmhan, Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model on decentralized data, addressing privacy concerns while leveraging …
Continue Reading
Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning
-
CDPG
-
January 9, 2025
-
Data Privacy
-
0 Comments
April 2024 Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning Authors: M. Yashwanth, G. K. Nayak, H. Rangwani, A. Singh, R. V. Babu, A. Chakraborty Federated Learning (FL) is an emerging machine learning framework that enables multiple clients (coordinated by a server) to collaboratively train a global model by aggregating the locally trained models without sharing any client’s training data. …
Continue Reading
Continual Mean Estimation Under User-Level Privacy
-
CDPG
-
January 9, 2025
-
Data Privacy
-
0 Comments
December 2022 Continual Mean Estimation Under User-Level Privacy Authors: A. J. George, L. Ramesh, A. V. Singh and H. Tyagi We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary …
Continue Reading
User-Level Differentially Private Mean Estimation for Real-World Datasets
-
CDPG
-
January 9, 2025
-
Data Privacy
-
0 Comments
User-Level Differentially Private Mean Estimation for Real-World Datasets Authors: V. A. Rameshwar, A. Tandon, and A. Sharma In this work, we provide rigorous theoretical justifications for the performance trends of well-known clipping-based algorithms on real-world ITMS and i.i.d. synthetic datasets. An important contribution of this work is the formalization and explicit computation of the “worst-case estimation error” incurred by a …
Continue Reading
Empowering SMPC: Bridging the Gap Between Scalability, Memory Efficiency and Privacy in Neural Network Inference
-
CDPG
-
January 9, 2025
-
Data Privacy
-
0 Comments
Jan 2024 Empowering SMPC: Bridging the Gap Between Scalability, Memory Efficiency and Privacy in Neural Network Inference Authors: R. Burra, A. Tandon and S. Mittal This paper aims to develop an efficient open-source Secure Multi-Party Computation (SMPC) repository, that addresses the issue of practical and scalable implementation of SMPC protocol on machines with moderate computational resources while aiming to reduce …
Continue Reading
Mean Estimation with User-Level Privacy for Spatio-Temporal IoT Datasets
-
CDPG
-
January 9, 2025
-
Data Privacy
-
0 Comments
April 2024 Mean Estimation with User-Level Privacy for Spatio-Temporal IoT Datasets Authors: P. Gupta, V. A. Rameshwar, A. Tandon and N. Chakraborty This paper considers the problem of the private release of sample means of speed values from traffic datasets. Our key contribution is the development of user-level differentially private algorithms that incorporate carefully chosen parameter values to ensure low …
Continue Reading