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FedAvg

Syntologyentry name in harvested coderead from the graph 2026-09-24

FedAvg appears in the code Syntology harvested for 21 papers, as 17 distinct code bodies found in 27 places (a place is one code body under one paper). At least one of them ran in 15 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named FedAvg do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 8 of the 17 distinct code bodies named FedAvg; 9 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
5ran · our draft was wrong
0ran · fixture could not drive it
3ran
9unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 11 of the 27 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

21 papers shown of 21, newest first; 27 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive, and the graph's for 2 papers added by Syntology. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing ⋆ added by Syntology 2026-03 (from id) yanzhenxing123/FastPFRec/model/graph/PerFedRec.py cddf6b6e878cc6ae ran · our draft was wrong Apache-2.0 (permissive)
FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing ⋆ added by Syntology 2026-03 (from id) yanzhenxing123/FastPFRec/model/graph/FastPFRec.py f9964d719c5cb8f4 ran · our draft was wrong Apache-2.0 (permissive)
MARS: A Malignity-Aware Backdoor Defense in Federated Learning added by Syntology 2025-09 (from id) yunming181920/MARS/defenses/mars.py 05179368187f7e1e unverified no licence file found · pointer only
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations 9 Sep 2024 atp-1010/federatedllm/fed_utils/model_aggregation.py 516cac45490ea6a7 unverified no licence file found · pointer only
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction 8 Aug 2024 sprinter1999/fedelc/fl_models/fed.py ee28ff7729e298ee ran MIT (permissive)
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition 28 Jun 2024 xinghaowu/feddecomp/models/Fed.py 9e794f046561a69a ran no licence file found · pointer only
Dual-Personalizing Adapter for Federated Foundation Models 28 Mar 2024 Lydia-yang/FedDPA/code/fed_utils/model_aggregation.py 5826791292da68e9 unverified no licence file found · pointer only
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning 22 Nov 2023 jhoon-oh/FedBABU/models/Fed.py f9964d719c5cb8f4 ran · our draft was wrong MIT (permissive)
Fusing Models with Complementary Expertise 2 Oct 2023 hwang595/FoE-ICLR2024/cifar10_non_iid_example.py cddf6b6e878cc6ae ran · our draft was wrong no licence file found · pointer only
Towards Building the Federated GPT: Federated Instruction Tuning 9 May 2023 jayzhang42/federatedgpt-shepherd/fed_utils/model_aggregation.py e4ba8feaeb71a2ff unverified Apache-2.0 (permissive)
Federated Learning with Label Distribution Skew via Logits Calibration 1 Sep 2022 bytedance/feddecorr/approach/fedlogitcal.py af8f3034a8ca7b6f ran MIT (permissive)
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution 20 Aug 2022 mmorafah/flis/src/fedavg/fedavg.py c42566650a9a2b17 unverified MIT (permissive)
FedBABU: Towards Enhanced Representation for Federated Image Classification 4 Jun 2021 jhoon-oh/fedbabu/models/Fed.py f9964d719c5cb8f4 ran · our draft was wrong MIT (permissive)
Specialized federated learning using a mixture of experts 5 Oct 2020 edvinli/federated-learning-mixture/models/FederatedAveraging.py 542a499a7eafaa04 unverified MIT (permissive)
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning 4 Sep 2020 hongyouc/FedBE/models/Fed.py 276295afe2705ae2 ran · our draft was wrong Apache-2.0 (permissive)
SplitFed: When Federated Learning Meets Split Learning 25 Apr 2020 chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning/SFLV1_ResNet_HAM10000.py cddf6b6e878cc6ae ran · our draft was wrong no licence file found · pointer only
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning 7 Apr 2020 weichennone/FedMAX/digit_object_recognition/models/Fed.py f9964d719c5cb8f4 ran · our draft was wrong MIT (permissive)
Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications 2 Jul 2019 mtang724/Self-Balancing-Federated-Learning/models/Fed.py f9964d719c5cb8f4 ran · our draft was wrong MIT (permissive)
Learning Private Neural Language Modeling with Attentive Aggregation 17 Dec 2018 shaoxiongji/federated-learning/models/Fed.py f9964d719c5cb8f4 ran · our draft was wrong MIT (permissive)
Federated Learning with Non-IID Data 2 Jun 2018 yjlee22/FedShare/src/strategy.py 75d8463105db0e97 ran · our draft was wrong MIT (permissive)
Progressive Growing of GANs for Improved Quality, Stability, and Variation 27 Oct 2017 peterhan91/thorax_gan/Federated_GAN/models/Fed.py f9964d719c5cb8f4 ran · our draft was wrong MIT (permissive)
Communication-Efficient Learning of Deep Networks from Decentralized Data 17 Feb 2016 identical code first harvested elsewhere 75d8463105db0e97 ran · our draft was wrong licence of this copy not recorded
Communication-Efficient Learning of Deep Networks from Decentralized Data 17 Feb 2016 hongshenghu/source-inference-fl/models/Fed.py f9964d719c5cb8f4 ran · our draft was wrong no licence file found · pointer only
Communication-Efficient Learning of Deep Networks from Decentralized Data 17 Feb 2016 HongshengHu/SIAs-Beyond_MIAs_in_Federated_Learning/FedAvg-SIAs/models/Fed.py 5719ee7d03438fcb ran · our draft was wrong no licence file found · pointer only
Communication-Efficient Learning of Deep Networks from Decentralized Data 17 Feb 2016 carbonati/fl-zoo/fl_zoo/optimizers/fed_avg.py ace0580d4c7a8494 unverified no licence file found · pointer only
Communication-Efficient Learning of Deep Networks from Decentralized Data 17 Feb 2016 TsingZ0/PFL-Non-IID/system/flcore/servers/serveravg.py 01b0d50f47833fc2 unverified Apache-2.0 (permissive)
Communication-Efficient Learning of Deep Networks from Decentralized Data 17 Feb 2016 thejungwon/gc-fed/algorithms/fedavg.py 32845aa08d8061c2 unverified no licence file found · pointer only

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections