Pith Number

pith:4NVBCJJV

pith:2026:4NVBCJJV3JX3UF6XGGCVHK22DZ

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Byzantine-Robust Distributed Sparse Learning Revisited

Kangqiang Li, Lixin Zhang, Yuxuan Wang

Local l1-regularized robust estimators plus server-side robust aggregation deliver non-asymptotic guarantees and near-optimal rates for Byzantine-robust distributed sparse learning.

arxiv:2605.13283 v1 · 2026-05-13 · cs.LG · math.ST · stat.TH

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4 Citations open

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Claims

C1strongest claim

the resulting estimators yield non-asymptotic guarantees and attain near-optimal statistical rates under mild conditions, while remaining communication-efficient

C2weakest assumption

mild conditions on the data distribution, sparsity level, and fraction of Byzantine machines (standard but unspecified in abstract; typically requires bounded moments and Byzantine fraction below 1/2)

C3one line summary

Local L1-regularized robust estimators plus server-side robust aggregation achieve near-optimal rates for high-dimensional sparse learning under Byzantine attacks.

References

16 extracted · 16 resolved · 1 Pith anchors

[1] Sparse Quantile Huber Regression for Efficient and Robust Estimation · arXiv:1402.4624

[2] Distributed testing and estimation under sparse high dimensional models. Ann. Statist. 46, 1352–1382. doi:10.1214/17-AOS1587. Belloni,A.,Chernozhukov,V.,2011.𝓁 1-penalizedquantileregressioninhigh-dime 2011 · doi:10.1214/17-aos1587

[3] 2023 , journal = 2021

[4] Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions. J. R. Stat. Soc. B: Stat. Methodol. 79, 247–265. doi:10.1111/rssb.12166. Fan, J., Liu, H., Sun, Q. · doi:10.1111/rssb.12166

[5] I-lamm for sparse learning: Simultaneous control of algorithmic complexity and statistical error. Ann. Statist. 46, 814–841. doi:10.1214/17-AOS1568. He, X., Pan, X., Tan, K.M., Zhou, W.X., · doi:10.1214/17-aos1568

Receipt and verification

First computed	2026-05-18T02:44:49.175230Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e36a112535da6fba17d7318553ab5a1e7b3cecfc4de2d543c10666270c2c7f37

Aliases

arxiv: 2605.13283 · arxiv_version: 2605.13283v1 · doi: 10.48550/arxiv.2605.13283 · pith_short_12: 4NVBCJJV3JX3 · pith_short_16: 4NVBCJJV3JX3UF6X · pith_short_8: 4NVBCJJV

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4NVBCJJV3JX3UF6XGGCVHK22DZ \
  | 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: e36a112535da6fba17d7318553ab5a1e7b3cecfc4de2d543c10666270c2c7f37

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "38f83b0b65db508330450c6154e16ae0088efbc6bbbbaa224404af58d776a32e",
    "cross_cats_sorted": [
      "math.ST",
      "stat.TH"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T10:00:23Z",
    "title_canon_sha256": "eb725248b003ae8dc6463d1d1adc6f6045a9af5e82703a8fbb0149686cc8bbb4"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13283",
    "kind": "arxiv",
    "version": 1
  }
}