Pith Number

pith:A7NOOQKB

pith:2026:A7NOOQKB4MMA5XGTWB3Y5J5OJG

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Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions

Ay\c{c}a \"Oz\c{c}elikkale, Sanja Karilanova, Subhrakanti Dey

Adaptation methods allow federated spiking neural networks to recover accuracy lost when clients sample data at different temporal resolutions.

arxiv:2605.15355 v1 · 2026-05-14 · cs.LG

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Claims

C1strongest claim

The proposed adaptation methods can substantially recover accuracy lost due to temporal mismatch, hence enabling each client to train at their local temporal resolution while remaining compatible with the global model.

C2weakest assumption

That neuron parameters learned at one temporal resolution can be meaningfully integrated with those from another resolution through the proposed aggregation rules without requiring changes to the underlying SNN architecture or loss of spike sparsity benefits.

C3one line summary

Federated learning framework for SNNs that adapts to heterogeneous temporal resolutions via neuron parameter integration, recovering accuracy on SHD and DVS-Gesture under varied mismatch scenarios.

References

47 extracted · 47 resolved · 0 Pith anchors

[1] Brendan McMahan 2021

[2] Parizi, and Fahad Saeed 2020

[3] Data centers on wheels: Emissions from computing onboard autonomous vehicles.IEEE Micro, 43(1):29–39, 2023 2023

[4] Bipin Rajendran, Abu Sebastian, Michael Schmuker, Narayan Srinivasa, and Evangelos Eleft- heriou. Low-power neuromorphic hardware for signal processing applications: A review of architectural and syst 2019

[5] Fonseca Guerra, Prasad Joshi, Philipp Plank, and Sumedh R 2021

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:54.121014Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

07dae74141e3180edcd3b0778ea7ae4986df52d3c956c927a2fb9e0ca6144e10

Aliases

arxiv: 2605.15355 · arxiv_version: 2605.15355v1 · doi: 10.48550/arxiv.2605.15355 · pith_short_12: A7NOOQKB4MMA · pith_short_16: A7NOOQKB4MMA5XGT · pith_short_8: A7NOOQKB

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/A7NOOQKB4MMA5XGTWB3Y5J5OJG \
  | 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: 07dae74141e3180edcd3b0778ea7ae4986df52d3c956c927a2fb9e0ca6144e10

Canonical record JSON

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    "abstract_canon_sha256": "de4c51f743d3744384ffb6de587c92551c4bcf95d5e3a957460c6d5634bac908",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T19:33:35Z",
    "title_canon_sha256": "07ccc9c749faaee69937c1b8d9c03ef469317b8fe7a30e89608d808041cf50fc"
  },
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  "source": {
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    "kind": "arxiv",
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