Deepesh Data
7 papers · 2019–2023 · 4 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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🌍
Conference Polyglot · Bronze
🌉
Interdisciplinary Bridge · Bronze
🗺️
Taxonomy Completionist
(17)
🐣
Hot Topic Early Bird
Venues
Top co-authors
Research topics
Machine Learning
(7)
Artificial Intelligence
(6)
Application Areas
(6)
Learning Paradigms · Artificial Intelligence
(5)
Mathematics & Optimization
(4)
Optimization
(3)
Optimization & Theory · Machine Learning
(3)
Deep Learning
(1)
Core AI
(1)
Learning Paradigms · Machine Learning
(1)
Learning Types
(1)
Mathematics
(1)
Optimization & Theory · Deep Learning
(1)
Keywords
federated learning
(5)
distributed optimization
(3)
stochastic gradient descent
(3)
communication efficiency
(2)
distributed learning
(2)
differential privacy
(2)
personalized model
(2)
empirical baye
(1)
gradient compression
(1)
distributed stochastic gradient descent
(1)
statistical heterogeneity
(1)
client-server architecture
(1)
privacy amplification
(1)
renyi differential privacy
(1)
error compensation
(1)
gradient quantization
(1)
robust mean estimation
(1)
shuffle model
(1)
local computation
(1)
byzantine resilience
(1)
Papers
A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy
ICLR 2023
Flexible Accuracy for Differential Privacy
AISTATS 2022
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data
ICML 2021
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
NeurIPS 2021
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning
NeurIPS 2021
Shuffled Model of Differential Privacy in Federated Learning
AISTATS 2021
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations
NeurIPS 2019