Pith. sign in

Paper Citation Record · LEDGER

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

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 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 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:54:41.604681Z

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.

source=arxiv_source observed=2026-08-09T19:54:41.604681Z digest=sha256:f97833bf9cfed08ed9ddedd1639329eef5ff7e8e08789233c050fba71ff58888

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.

source=arxiv_source observed=2026-08-04T21:06:26.439936Z digest=sha256:e200845c4a06f3d601c36753bb99664d2cafe93ea073b49db3a6ed1905b65051

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-11T02:52:53.588595Z digest=sha256:c660e81f1b8e35086910cc7e2105452ef57a788dd9b3e4ac04d7916771eebe62

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-12T01:51:20.003552Z digest=sha256:1bda8047649837551f6d76aad252b8053471fa7dd1e9e155427404a8945a8c50

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-20T13:08:52.912250Z digest=sha256:e26c87d07cc766c651e047cf66500180b7bbe75377aceda1c352fadae50833d8

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-21T05:49:28.713982Z digest=sha256:b29412f6c1163319dbb884f584b258d2725f72c0032a9fb18949bf6394fe85ce

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-28T23:07:05.700294Z digest=sha256:bbd6c94b414d78deb7114abf860c7f24673777fd911cded88c82423f4c36123f