Pith Number

pith:JPEGGRCG

pith:2026:JPEGGRCGVGCS6CWF6DOWPPSBKK

not attested not anchored not stored refs resolved

DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning

Anastasios Drosou, Dimitrios Alexopoulos, Giorgos Tatsis, Haaris Mehmood, Jie Xu, Karthikeyan Saravanan, Mete Ozay

DisAgg distributes aggregation to a small client committee via secret sharing to cut secure FL computation while preserving privacy.

arxiv:2605.13708 v1 · 2026-05-13 · cs.CR · cs.DC · cs.LG

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\usepackage{pith}
\pithnumber{JPEGGRCGVGCS6CWF6DOWPPSBKK}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

DisAgg processes 100k-dimensional update vectors from 100k 5G clients with a 4.6x speedup compared to OPA, the previous best protocol, while preserving privacy against a curious server and a limited fraction of colluding clients.

C2weakest assumption

The protocol assumes that secret sharing to the aggregator committee provides privacy guarantees without additional overheads and that the limited fraction of colluding clients does not exceed the threshold that would break reconstruction or privacy.

C3one line summary

DisAgg distributes secure aggregation to a client committee via secret sharing, eliminating local masking and homomorphic encryption while preserving privacy and delivering 4.6x speedup over OPA for 100k clients and 100k-dimensional updates.

References

49 extracted · 49 resolved · 0 Pith anchors

[1] ACM CCS 2017 , booktitle = 2017

[2] Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) , booktitle = 2017

[3] Proceedings of Machine learning and systems , volume=

[4] International Conference on Learning Representations , year=

[5] arXiv preprint arXiv:2107.06917 , year= 2021

Receipt and verification

First computed	2026-05-18T02:44:16.787788Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4bc8634446a9852f0ac5f0dd67be4152832687a787bd36c99fcd69056ab531cf

Aliases

arxiv: 2605.13708 · arxiv_version: 2605.13708v1 · doi: 10.48550/arxiv.2605.13708 · pith_short_12: JPEGGRCGVGCS · pith_short_16: JPEGGRCGVGCS6CWF · pith_short_8: JPEGGRCG

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JPEGGRCGVGCS6CWF6DOWPPSBKK \
  | 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: 4bc8634446a9852f0ac5f0dd67be4152832687a787bd36c99fcd69056ab531cf

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2ff4a8cff89db678e1c2c3639c0c96cc0adfd08a4e76cd4066ca869e3cca6b81",
    "cross_cats_sorted": [
      "cs.DC",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CR",
    "submitted_at": "2026-05-13T15:56:12Z",
    "title_canon_sha256": "03e4a5cad730bf039cb9e552c0856ae5864e0ffc3450771d6d86e203fc7d95e7"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13708",
    "kind": "arxiv",
    "version": 1
  }
}