Concepedia

Concept

distributed machine learning

Parents

428

Publications

35.5K

Citations

1.6K

Authors

540

Institutions

About

Distributed machine learning is a field of research and a methodological approach focused on developing and applying machine learning algorithms across multiple interconnected computing nodes. It investigates techniques for partitioning data and computational tasks, managing communication and synchronization, and ensuring the scalability, efficiency, and convergence of learning processes in decentralized environments, thereby enabling the training of models on datasets exceeding the capacity of a single machine and facilitating collaborative or privacy-preserving learning paradigms.

Top Authors

Rankings shown are based on concept H-Index.

MI

University of California, Berkeley

EP

Carnegie Mellon University

ES

University of California, Berkeley

JG

Tsinghua University

SW

Tsinghua University

Top Institutions

Rankings shown are based on concept H-Index.

Microsoft (United States)

Redmond, United States

University of California, Berkeley

Berkeley, United States

Tsinghua University

Beijing, China

Pittsburgh, United States

Shenzhen, China

Top Venues

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