Federated Averaging: local model updates with server-side aggregation

What changed

Formalized communication-constrained learning over distributed client data and evaluated repeated local SGD steps followed by weighted model averaging, including non-IID and unbalanced partitions.

Scientific scope

Formalized communication-constrained learning over distributed client data and evaluated repeated local SGD steps followed by weighted model averaging, including non-IID and unbalanced partitions. Inherits distributed model averaging; contextual advance is the federated problem/evaluation and practical algorithm, not invention of all distributed learning.2016 v1 four authors;2017 v3 five with Seth Hampson. Local data remaining on devices does not make updates private, secure or differentially private. Non-IID robustness/communication reductions are empirical, not universal convergence for arbitrary heterogeneous clients.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits distributed model averaging; contextual advance is the federated problem/evaluation and practical algorithm, not invention of all distributed learning.2016 v1 four authors;2017 v3 five with Seth Hampson. Local data remaining on devices does not make updates private, secure or differentially private. Non-IID robustness/communication reductions are empirical, not universal convergence for arbitrary heterogeneous clients.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Federated Learning of Deep Networks using Model Averaging
• Communication-Efficient Learning of Deep Networks from Decentralized Data

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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