Methods › General › Data Parallel Methods › Local SGD

Local SGD

69 papers tagged archive 2025-07-28

Introduced by Sebastian U. Stich in Local SGD Converges Fast and Communicates Little

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Local SGD is a distributed training technique that runs SGD independently in parallel on different workers and averages the sequences only once in a while.

PaperSource

Papers archive 2025-07-28

30 shown of 69, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 23 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Federated Learning27
Distributed Optimization11
Image Classification4
Language Modeling4
Language Modelling4
image-classification4
Edge-computing3
Stochastic Optimization3
BIG-bench Machine Learning2
Blocking2
Privacy Preserving2
2k1
CPU1
Deep Learning1
Domain Generalization1
GPU1
Large Language Model1
Machine Translation1
Multi-Task Learning1
Personalized Federated Learning1

Usage over time archive 2025-07-28

Papers per year tagged with Local SGD: 2018 to 2025, peak 19 19 0 2018: 1 paper 2018 2019: 5 papers 2019 2020: 11 papers 2020 2021: 19 papers 2021 2022: 10 papers 2022 2023: 11 papers 2023 2024: 9 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (69 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Data Parallel MethodsDistributed MethodsOptimizationStochastic Optimization

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