Paper Citation Record · LEDGER

Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2006.14591.

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

pith.paper-citation-record.v1
2006.14591 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:14.900172Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T00:12:50.697819Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3f2e247e-b4ff-40a3-b8e9-d6907decc848 · inbound

BICompFL: Stochastic Federated Learning with Bi-Directional Compression cites this paper.

BICompFL: Stochastic Federated Learning with Bi-Directional Compression Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T19:54:41.604681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e6b6b8f-4b7a-49eb-a9b6-d5cb083028f2 · inbound

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees

Reference 179

Resolution: unresolved; no resolver link, observed 2026-08-04T21:06:26.439936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 828a48a8-3c9e-48e7-8f90-a8832e20e5f4 · inbound

Scalable Distributed Stochastic Optimization via Bidirectional Compression: Beyond Pessimistic Limits cites this paper.

Scalable Distributed Stochastic Optimization via Bidirectional Compression: Beyond Pessimistic Limits Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:05:54.904432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4cc05a88-27aa-474f-9872-26c1a55c6c2d · inbound

Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction cites this paper.

Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees

Reference 187

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:51:33.116980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1a874812-1234-4bda-87d2-e8bda589862b · inbound

Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method cites this paper.

Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees

Reference 189

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:13:18.609560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d3f9f975-659a-4e5b-90a9-ef4267941095 · inbound

LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging cites this paper.

LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees

Reference 190

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:49:40.539745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 69ee2904-0019-4113-9c7d-578e88cf9b51 · inbound

A Tight Theory of Error Feedback Algorithms in Distributed Optimization cites this paper.

A Tight Theory of Error Feedback Algorithms in Distributed Optimization Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-06-29T00:12:50.701309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1e5a1439-a018-409d-beda-5dd8065a52fa · inbound

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization cites this paper.

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees

Reference 215

Resolution: unresolved; no resolver link, observed 2026-08-15T14:39:14.900172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.