Pith Number
pith:BIHBH2SP
pith:2024:BIHBH2SP2CT5MPBX6ML2G6O7DF
not attested
not anchored
not stored
refs pending
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
arxiv:2411.10673 v1 · 2024-11-16 · cs.LG · cs.CR
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{BIHBH2SP2CT5MPBX6ML2G6O7DF}
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
· sign in to
claim
4
Citations
5
Replications
✓
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 | 2026-07-05T09:36:51.524221Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0a0e13ea4fd0a7d63c37f317a379df1960ecd762b368f3f099f966c1b195d987
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BIHBH2SP2CT5MPBX6ML2G6O7DF \
| 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: 0a0e13ea4fd0a7d63c37f317a379df1960ecd762b368f3f099f966c1b195d987
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "b45b4ddbae64dce245c05f03c6e8557d4046e047a21bd6444fc7ba07069a654c",
"cross_cats_sorted": [
"cs.CR"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2024-11-16T02:25:05Z",
"title_canon_sha256": "c84847b3b286dde9f1403ecd2bd3e964af1e25c7a2ef20c19df86084716271ec"
},
"schema_version": "1.0",
"source": {
"id": "2411.10673",
"kind": "arxiv",
"version": 1
}
}