Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks

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 …

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Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning

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. …

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Continual Mean Estimation Under User-Level Privacy

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 …

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User-Level Differentially Private Mean Estimation for Real-World Datasets

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 …

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Empowering SMPC: Bridging the Gap Between Scalability, Memory Efficiency and Privacy in Neural Network Inference

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 …

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Mean Estimation with User-Level Privacy for Spatio-Temporal IoT Datasets

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 …

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