Pith Number
pith:ONE7D7IH
pith:2024:ONE7D7IHLC3VELMG5JIVWUNFS4
not attested
not anchored
not stored
refs pending
How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?
arxiv:2412.08282 v2 · 2024-12-11 · cs.LG · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{ONE7D7IHLC3VELMG5JIVWUNFS4}
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
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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 | 2026-07-05T09:51:39.607289Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
7349f1fd0758b7522d86ea515b51a59718bd3099d721e09c25c2ea4162d3d383
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ONE7D7IHLC3VELMG5JIVWUNFS4 \
| 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: 7349f1fd0758b7522d86ea515b51a59718bd3099d721e09c25c2ea4162d3d383
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "b8409cc2793d0ca819360703ff78f4506dac06325836be78df81e90d99fb589b",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2024-12-11T10:57:16Z",
"title_canon_sha256": "4402d3e596d849af7921f18756fb3d07f075a2b76564fae11afb7bcd21c1266d"
},
"schema_version": "1.0",
"source": {
"id": "2412.08282",
"kind": "arxiv",
"version": 2
}
}