424 citations · 542 across the 22 of their papers we have counts for
8 papers · 1 filter
Data Measurements for Decentralized Data Markets
Charles Lu, Mohammad Mohammadi Amiri, Ramesh Raskar
Decentralized data markets can provide more equitable forms of data acquisition for machine learning. However, to realize practical marketplaces, efficient techniques for seller se…
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models
Abhishek Singh, Gauri Gupta, Ritvik Kapila +5
Federated Learning (FL) enables collaborative optimization of machine learning models across decentralized data by aggregating model parameters. Our approach extends this concept b…
Federated Conformal Predictors for Distributed Uncertainty Quantification
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy +2
Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing ste…
Fundamentals of Task-Agnostic Data Valuation
Mohammad Mohammadi Amiri, Frederic Berdoz, Ramesh Raskar
We study valuing the data of a data owner/seller for a data seeker/buyer. Data valuation is often carried out for a specific task assuming a particular utility metric, such as test…
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
Sihun Baek, Jihong Park, Praneeth Vepakomma +3
This article seeks for a distributed learning solution for the visual transformer (ViT) architectures. Compared to convolutional neural network (CNN) architectures, ViTs often have…
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
Shraman Pal, Mansi Uniyal, Jihong Park +5
In recent years, there have been great advances in the field of decentralized learning with private data. Federated learning (FL) and split learning (SL) are two spearheads possess…