pith:T3XG2TU4
Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection
Dynamic stochastic sharding and linear integrity tags let banks train shared fraud models with linear communication costs and verifiable updates.
arxiv:2604.23437 v2 · 2026-04-25 · cs.CR · cs.LG
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\pithnumber{T3XG2TU44B5IPFFBH4TV7O3VG2}
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Record completeness
Claims
DSFL replaces mesh topologies with Dynamic Stochastic Sharding, reducing communication complexity from O(N^2) to O(N m), ... Empirical evaluation on the Credit Card Fraud Detection Dataset (ULB) demonstrates an approximately 33x latency reduction compared to Paillier-based secure aggregation, while maintaining strong resilience under simulated failures.
The assumption that Linear Integrity Tags suffice for integrity without enforcing semantic correctness of updates and that dynamic sharding preserves privacy against gradient inversion attacks, as stated in the abstract's description of the mechanisms.
DSFL introduces dynamic stochastic sharding and linear integrity tags to reduce federated learning communication from quadratic to linear while enabling verifiable aggregation, achieving 33x lower latency than Paillier on credit card fraud data.
Receipt and verification
| First computed | 2026-05-20T00:03:12.545038Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
9eee6d4e9ce07a8794a13f275fbb753693f0beb48eaea6d220922b53411a3670
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/T3XG2TU44B5IPFFBH4TV7O3VG2 \
| 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: 9eee6d4e9ce07a8794a13f275fbb753693f0beb48eaea6d220922b53411a3670
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
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"submitted_at": "2026-04-25T20:49:22Z",
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