Pith Number
pith:DKNQ6TSO
pith:2023:DKNQ6TSONTLM5RUIZ4FHW74IMT
not attested
not anchored
not stored
refs pending
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity
arxiv:2308.03521 v1 · 2023-08-04 · cs.LG · cs.AI · cs.DC
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{DKNQ6TSONTLM5RUIZ4FHW74IMT}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-07-05T06:38:18.122514Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
1a9b0f4e4e6cd6cec688cf0a7b7f8864d64239e75a04651f3671a9dc3087971d
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DKNQ6TSONTLM5RUIZ4FHW74IMT \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1a9b0f4e4e6cd6cec688cf0a7b7f8864d64239e75a04651f3671a9dc3087971d
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "92eabdd2dca92a6386257035e8578e916ce345bf98a1bf5a74ded0a72dd29abd",
"cross_cats_sorted": [
"cs.AI",
"cs.DC"
],
"license": "http://creativecommons.org/publicdomain/zero/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2023-08-04T04:18:01Z",
"title_canon_sha256": "0c066da5b27f6e76fe313e850e6a297a399fc84066322162f0f439d4e8c9d52c"
},
"schema_version": "1.0",
"source": {
"id": "2308.03521",
"kind": "arxiv",
"version": 1
}
}