Claim · 2016-02-17 · publication · Stated in source · Supported

The paper presents a method for federated learning of deep networks that the authors state is robust to unbalanced and non-IID data distributions.

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What it connects

Communication-Efficient Learning of Deep Networks from Decentralized Data → states its contribution → The paper presents a method for federated learning of deep networks that the authors state is robust to unbalanced and non-IID data distributions.

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Claim and evidence

Communication-Efficient Learning of Deep Networks from Decentralized Datastates its contribution ↓The paper presents a method for federated learning of deep networks that the authors state is robust to unbalanced and non-IID data distributions.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2016-02-17 · publication Records dated 2016 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

Communication-Efficient Learning of Deep Networks from Decentralized Data

arXiv · 2016-02-17

Cite the original source

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Source excerpt

“We present a practical method for the federated learning of deep networks that proves robust to the unbalanced and non-IID data distributions that naturally arise.”

Location in source arXiv v1 (titled "Federated Learning of Deep Networks using Model Averaging"), PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: H. Brendan McMahan; Eider Moore; Daniel Ramage
Version: arXiv:1602.05629v1, submitted 17 February 2016 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 5e5f5264bc409fbe1c43a6ac011b701e14b04fdf784a0deae47b39122819402c). The author list is the byline as printed on the first page.
Version date: 2016-02-17
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: H. Brendan McMahan, Eider Moore, Daniel Ramage, “Communication-Efficient Learning of Deep Networks from Decentralized Data”, 2016-02-17.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (25 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. A cross-family machine check (2026-10-30, item B052) found the quotation at its locator. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 17 February 2016 (UTC); later versions may word it differently.

Record ID kp-states-communication-efficient-learning-deep-networks-2016-2