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researcher

Saurav Prakash

University of Southern California

16 papers hereh-index 15848 citations25 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author10

Across the 15 of 16 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.DC5
  • cs.CR1
  • cs.CV1
  • eess.SY1
  • stat.ML1
affiliations
  • University of Southern California
same name
  • Saurav Prakash — 7 papers, h 1
  • Saurav Prakash — 5 papers, h 2
  • Saurav Prakash — 1 paper, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172024
most citedCoded Computing for Low-Latency Federated Learning over Wireless Edge Networks

99 citations · 260 across the 15 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.DC2020★ 99 cited

Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks

Saurav Prakash, Sagar Dhakal, Mustafa Akdeniz +4

Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federa…

cs.DC2020★ 2 cited

Secure and Fault Tolerant Decentralized Learning

Saurav Prakash, Hanieh Hashemi, Yongqin Wang +2

Federated learning (FL) is a promising paradigm for training a global model over data distributed across multiple data owners without centralizing clients' raw data. However, shari…

cs.DC2020★ 10 cited

Coded Computing for Federated Learning at the Edge

Saurav Prakash, Sagar Dhakal, Mustafa Akdeniz +2

Federated Learning (FL) is an exciting new paradigm that enables training a global model from data generated locally at the client nodes, without moving client data to a centralize…

cs.LG2020★ 66 cited

Coded Federated Learning

Sagar Dhakal, Saurav Prakash, Yair Yona +2

Federated learning is a method of training a global model from decentralized data distributed across client devices. Here, model parameters are computed locally by each client devi…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.