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Praneeth Vepakomma

Researcher at Massachusetts Institute of Technology

Publications -  40
Citations -  4760

Praneeth Vepakomma is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Deep learning & Distance correlation. The author has an hindex of 14, co-authored 40 publications receiving 2038 citations. Previous affiliations of Praneeth Vepakomma include Motorola & Motorola Solutions.

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Advances and open problems in federated learning

Peter Kairouz, +58 more
TL;DR: In this article, the authors describe the state-of-the-art in the field of federated learning from the perspective of distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, and statistics.
Posted Content

Split learning for health: Distributed deep learning without sharing raw patient data

TL;DR: This paper compares performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN.
Posted Content

FedML: A Research Library and Benchmark for Federated Machine Learning

TL;DR: FedML is introduced, an open research library and benchmark that facilitates the development of new federated learning algorithms and fair performance comparisons and can provide an efficient and reproducible means of developing and evaluating algorithms for the Federated learning research community.