no code implementations • 13 Feb 2024 • Yixiang Yao, Fei Wang, Srivatsan Ravi, Muhao Chen
Language Models as a Service (LMaaS) offers convenient access for developers and researchers to perform inference using pre-trained language models.
no code implementations • 7 Aug 2023 • Yixiang Yao, Weizhao Jin, Srivatsan Ravi
We propose a novel blind annotation protocol based on homomorphic encryption that allows domain oracles to collaboratively label ground truths without sharing data in plaintext with other parties.
1 code implementation • 20 Mar 2023 • Weizhao Jin, Yuhang Yao, Shanshan Han, Carlee Joe-Wong, Srivatsan Ravi, Salman Avestimehr, Chaoyang He
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data.
no code implementations • 11 May 2022 • Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil Dhinagar, Tanmay Ghai, Rafael Chrysovalantis Anastasiou, Armaghan Asghar, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite
Each site trains the neural network over its private data for some time, then shares the neural network parameters (i. e., weights, gradients) with a Federation Controller, which in turn aggregates the local models, sends the resulting community model back to each site, and the process repeats.
1 code implementation • NeurIPS 2023 • Yuhang Yao, Weizhao Jin, Srivatsan Ravi, Carlee Joe-Wong
Methods for training models on graphs distributed across multiple clients have recently grown in popularity, due to the size of these graphs as well as regulations on keeping data where it is generated.
no code implementations • 7 Aug 2021 • Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location.
1 code implementation • 5 Feb 2015 • Vitaly Aksenov, Vincent Gramoli, Petr Kuznetsov, Srivatsan Ravi, Di Shang
Designing an efficient concurrent data structure is an important challenge that is not easy to meet.
Distributed, Parallel, and Cluster Computing