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Ramesh Raskar — most-cited papers & profile · Federated Learning
← authors
·
overview
Ramesh Raskar
21
papers ·
1104
citations ·
82
h-index
Massachusetts Institute of Technology
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
FedML: A Research Library and Benchmark for Federated Machine Learning
2020 · 358 citations
Split learning for health: Distributed deep learning without sharing raw patient data
2018 · 179 citations
No Peek: A Survey of private distributed deep learning
2018 · 47 citations
Detailed comparison of communication efficiency of split learning and federated learning
2019 · 44 citations
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning
2021 · 14 citations
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
2021 · 12 citations
NoPeek: Information leakage reduction to share activations in distributed deep learning
2020 · 10 citations
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
2022 · 4 citations
Differentially Private CutMix for Split Learning with Vision Transformer
2022 · 4 citations
Scalable Collaborative Learning via Representation Sharing
2022 · 1 citations
Federated Conformal Predictors for Distributed Uncertainty Quantification
2023 · 1 citations
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models
2024 · 1 citations
DAVED: Data Acquisition via Experimental Design for Data Markets
2024 · 1 citations
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy
2024 · 1 citations
Data Measurements for Decentralized Data Markets
2024 · 1 citations
Top co-authors
Praneeth Vepakomma
· 11
Abhishek Singh
· 4
Otkrist Gupta
· 4
Charles Lu
· 3
Jihong Park
· 3
Mehdi Bennis
· 3
Abhishek Singh
· 2
Sai Praneeth Karimireddy
· 2
Seong-Lyun Kim
· 2
Sihun Baek
· 2
Tristan Swedish
· 2
Yichuan Shi
· 2
Topics
Federated Learning
Privacy
Optimization
Communication
Data Heterogeneity
Security
Encryption
Edge Computing
cs.LG
stat.ML