Performance Characterization of Containerized DNN Training and Inference on Edge Accelerators

December 2023 Performance Characterization of Containerized DNN Training and Inference on Edge Accelerators Authors: Prashanthi S.K., Vinayaka Hegde, Keerthana Patchava, Ankita Das and Yogesh Simmhan Edge devices have typically been used for DNN in-ferencing. The increase in the compute power of accelerated edges is leading to their use in DNN training also. As privacy becomes a concern on multi-tenant edge …

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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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A New Era of Data for Public Good: CDPG 2024 Symposium Highlights and Future Directions

The Symposium on Data for Public Good 2024 was a resounding success, bringing together thought leaders, practitioners, and innovators from across the globe to explore the transformative potential of data in addressing societal challenges. Held in JN Tata Auditorium, in IISc, on September 19-20, the symposium served as a vibrant platform for knowledge sharing, networking, and collaboration. Imagine a room …

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ADeX Powers New Era of Smart Agri-Credit Services

Hyderabad/Bangalore: The Centre for Data for Public Good (CDPG), FSID, Indian Institute of Science, Bengaluru, is pleased to announce the beginning of a new era of smart agri-credit services with the launch of the Smart Agri-Credit Services initiative by the Government of Telangana in collaboration with the HDFC Bank. Powered by the Agricultural Data Exchange (ADeX), this transformative initiative is …

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Symposium on Data for Public Good 2024 – 2nd edition concludes with a power-packed display of data-driven ideas and innovations for societal transformation

Bengaluru, India – Sep 26, 2024: The Centre of Data for Public Good (CDPG) at Foundation for Science, Innovation and Development, Indian Institute of Science (IISc) recently hosted the highly anticipated second edition of the Symposium on Data for Public Good in Bengaluru from 19-20th September 2024. The event shone a spotlight on data’s transformative power to address societal challenges …

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