Pith Number

pith:TDMWDCCJ

pith:2026:TDMWDCCJNVWZP5AZ36N5X7AVIE

not attested not anchored not stored refs resolved

BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoring

Hen-Wei Huang, Tiancheng Cao, Zixuan Shu

BiFedKD uses bidirectional knowledge distillation with temperature-scaled aggregation to align ECG clients under non-IID and long-tailed label distributions while cutting communication and computation costs.

arxiv:2605.14886 v1 · 2026-05-14 · cs.AI

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\pithnumber{TDMWDCCJNVWZP5AZ36N5X7AVIE}

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4 Citations open

5 Replications open

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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.

Claims

C1strongest claim

Experiments on the MIT-BIH Arrhythmia dataset show that BiFedKD improves accuracy and Macro-F1 over the baseline by 3.52% and 9.93%, respectively. Moreover, to reach the same Macro-F1, BiFedKD reduces communication overhead by 40% and computation cost by 71.7% compared with the baseline.

C2weakest assumption

That the aggregation-by-distillation pipeline with temperature scaling produces a stable global distillation signal sufficient to align clients under non-IID and long-tailed ECG label distributions.

C3one line summary

BiFedKD improves ECG classification accuracy by 3.52% and Macro-F1 by 9.93% on MIT-BIH while cutting communication overhead 40% and computation cost 71.7% versus baseline federated methods.

References

15 extracted · 15 resolved · 1 Pith anchors

[1] Internet of medical things: A systematic review, 2023 · doi:10.1016/j.neucom.2023.126719

[2] Federated machine learning: Concept and applications, 2019

[3] Federated learning for privacy preservation in smart healthcare systems: A comprehensive survey, 2023

[4] Fedsl: Federated split learning for collaborative healthcare analytics on resource-constrained wearable iomt devices, 2024

[5] Distilling the Knowledge in a Neural Network 2015 · arXiv:1503.02531

Receipt and verification

First computed	2026-05-17T23:38:55.993658Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

98d96188496d6d97f419df9bdbfc15413f31c5f6da704c1426a48824e4ffc1c5

Aliases

arxiv: 2605.14886 · arxiv_version: 2605.14886v1 · doi: 10.48550/arxiv.2605.14886 · pith_short_12: TDMWDCCJNVWZ · pith_short_16: TDMWDCCJNVWZP5AZ · pith_short_8: TDMWDCCJ

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Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TDMWDCCJNVWZP5AZ36N5X7AVIE \
  | 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: 98d96188496d6d97f419df9bdbfc15413f31c5f6da704c1426a48824e4ffc1c5

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "8b8d30ac5fd62e2442b045c71792f269074f305949ecdc3875cb6de11c5dee30",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-14T14:31:02Z",
    "title_canon_sha256": "5f6fc59f4a00be3f606ef2926acbba653fa671d044ea978dc0278804f4b015cc"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14886",
    "kind": "arxiv",
    "version": 1
  }
}