354 citations · 956 across the 24 of their papers we have counts for
7 papers · 1 filter
Split Learning for collaborative deep learning in healthcare
Maarten G. Poirot, Praneeth Vepakomma, Ken Chang +3
Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-cen…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish +3
Recently, there has been the development of Split Learning, a framework for distributed computation where model components are split between the client and server (Vepakomma et al.…
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish +3
In this work we introduce ExpertMatcher, a method for automating deep learning model selection using autoencoders. Specifically, we are interested in performing inference on data s…
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest
Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh +3
In this paper we investigate the usage of adversarial perturbations for the purpose of privacy from human perception and model (machine) based detection. We employ adversarial pert…
Detailed comparison of communication efficiency of split learning and federated learning
Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta +1
We compare communication efficiencies of two compelling distributed machine learning approaches of split learning and federated learning. We show useful settings under which each m…
Data Markets to support AI for All: Pricing, Valuation and Governance
Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish +1
We discuss a data market technique based on intrinsic (relevance and uniqueness) as well as extrinsic value (influenced by supply and demand) of data. For intrinsic value, we expla…