Pith Number

pith:UJBTDMCG

pith:2026:UJBTDMCGB4XF4QFO3L73JIIDOF

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Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework

Chenghao Huang, Hao Wang, Xiaolu Chen, Yanru Zhang

A federated learning framework fuses solar irradiance data via co-attention to detect photovoltaic generation fraud while keeping household data private across communities.

arxiv:2605.17039 v1 · 2026-05-16 · cs.LG · cs.CE

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Claims

C1strongest claim

Extensive experiments on a real-world residential PV dataset validate the effectiveness of the developed method and demonstrate that it outperforms state-of-the-art FL methods across various scenarios, with scalability across community sizes and strong robustness to class imbalance.

C2weakest assumption

The assumption that discrepancies between reported PV generation and fused solar irradiance/weather data reliably indicate fraud, and that prototype alignment sufficiently mitigates class imbalance without distorting normal generation patterns (implicit in the co-attention and FL aggregation description).

C3one line summary

A federated learning framework fuses solar irradiance and PV generation data via co-attention, uses prototype alignment for imbalance, and aggregates models across communities to detect generation fraud while preserving privacy.

References

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[1] International Energy Agency, “Renewables 2024,” 2023. [Online]. Available: https://www.iea.org/reports/renewables-2024 2024

[2] Renewable energy market update, 2023

[3] A review of cyber security risks of power systems: from static to dynamic false data attacks, 2020

[4] Detection methods in smart meters for electricity thefts: A survey, 2022

[5] Deep learning detection of electricity theft cyber-attacks in renewable distributed generation, 2020

Receipt and verification

First computed	2026-05-20T00:03:37.273148Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a24331b0460f2e5e40aedaffb4a10371701bc223425013115f83a45f5db982fe

Aliases

arxiv: 2605.17039 · arxiv_version: 2605.17039v1 · doi: 10.48550/arxiv.2605.17039 · pith_short_12: UJBTDMCGB4XF · pith_short_16: UJBTDMCGB4XF4QFO · pith_short_8: UJBTDMCG

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UJBTDMCGB4XF4QFO3L73JIIDOF \
  | 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: a24331b0460f2e5e40aedaffb4a10371701bc223425013115f83a45f5db982fe

Canonical record JSON

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    "abstract_canon_sha256": "7a2680599d8a6f20ca330fa05c61dfa3463e2a4843fe4ae57fa0de3d4180f3ad",
    "cross_cats_sorted": [
      "cs.CE"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-16T15:19:14Z",
    "title_canon_sha256": "a1e462e823449e2622158f3d0f312cda22a8399d761a22166e2f5b27e7641de6"
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  "source": {
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    "kind": "arxiv",
    "version": 1
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