Paper Citation Record · LEDGER

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling

As of 22 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2505.21695.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.21695 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:33:45.476302Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

11 of 11 outbound references displayed

verified exact0
verified fuzzy11
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3d426f83-44cf-4676-b1f9-970b8a6438ea · outbound

This paper cites Federated learning based on dynamic regularization.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Federated learning based on dynamic regularization

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:48.169060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation efc10033-cb09-4afd-87db-95b2b3a0fb76 · outbound

This paper cites Altomare, D.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Altomare, D

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:47.864545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 597c7514-d8da-4e1d-8a8a-cdd5eaace9a7 · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learning.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Scaffold: Stochastic controlled averaging for federated learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:47.655653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ea009d4a-af5a-472e-89e9-cfdfedbb4d7a · outbound

This paper cites Mime: Mimicking centralised stochastic algorithms in federated learning.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Mime: Mimicking centralised stochastic algorithms in federated learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:47.394017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d7bb0bd7-0871-4395-8997-605b7a952cf9 · outbound

This paper cites Federated optimization in heterogeneous networks.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Federated optimization in heterogeneous networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:47.197235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d85b1577-254f-4e91-9801-5790e294130c · outbound

This paper cites Communication- efficient learning of deep networks from decentralized data.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Communication- efficient learning of deep networks from decentralized data

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:46.994217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d2b4cb94-f8e3-467b-a5b4-d0dea15da7d9 · outbound

This paper cites Communication-efficient distributed optimization using an approximate newton-type method.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Communication-efficient distributed optimization using an approximate newton-type method

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:46.732727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8a3fa52a-50a4-4497-a5fc-11bac956e5c8 · outbound

This paper cites Local sgd converges fast and communicates little.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Local sgd converges fast and communicates little

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:46.499566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6def3558-bc92-49c0-aa78-2b647b9c80be · outbound

This paper cites Tackling the objective inconsistency problem in heterogeneous federated optimization.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Tackling the objective inconsistency problem in heterogeneous federated optimization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:46.256725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b28cc6b1-45ab-420a-8edd-8c287d6a4213 · outbound

This paper cites Op- timizing federated learning on non-iid data with reinforcement learning.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Op- timizing federated learning on non-iid data with reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:45.944794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8a21d11b-82a2-45af-aa31-0a44b2533e8d · outbound

This paper cites Disco: Distributed optimization for self-concordant empirical loss.

AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling Disco: Distributed optimization for self-concordant empirical loss

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:33:45.716503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.