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Paper Citation Record · LEDGER

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer

As of 21 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2601.22274.

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

pith.paper-citation-record.v1
2601.22274 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:46:21.725143Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

20 of 20 outbound references displayed

  • verified exact3
  • verified fuzzy4
  • unresolved12
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d9bfbc4c-fcca-4330-9bb5-3af5302c0313 · outbound

This paper cites Elastic Weight Consolidation (EWC): Nuts and Bolts.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Elastic Weight Consolidation (EWC): Nuts and Bolts

Reference 1

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no resolver link, observed 2026-08-15T15:46:21.630688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.630688Z digest=sha256:690197ad82d45b0c3b1152caf01173d50911cf15fe0f6fabb13df7beed86fece

Observation e366e71f-7836-42d2-9d6e-9cd5d9c271d9 · outbound

This paper cites CoDeC: Communication-Efficient Decentralized Continual Learning.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer CoDeC: Communication-Efficient Decentralized Continual Learning

Reference 3

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verified exact
local_arxiv, observed 2026-08-15T15:46:22.022080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T15:46:21.641342Z digest=sha256:45094d838c0e59408ef075b55bb0b32c6157489d23359fb5486b5bcd1d515b30

Observation 54925f22-116b-4f68-951f-6d915052bc8c · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Reading digits in natural images with unsupervised feature learning

Reference 7

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no resolver link, observed 2026-08-15T15:46:21.661810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.661810Z digest=sha256:dc472bcf0805df59d681b2ec40fd7e7035e2fb63aa7a8c9aad979319069f967d

Observation 059c6a85-22eb-438e-9ba7-43b26d75c08d · outbound

This paper cites Better generative replay for continual federated learning.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Better generative replay for continual federated learning

Reference 8

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no resolver link, observed 2026-08-15T15:46:21.666507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.666507Z digest=sha256:bbf06500dbef1f2852e118aecb7d01db09a782b30924ca15b1cf1155eb2396c4

Observation 157f6c99-b045-410e-b194-da545d716ec9 · outbound

This paper cites Adaptive Federated Optimization.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Adaptive Federated Optimization

Reference 9

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no resolver link, observed 2026-08-15T15:46:21.671639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.671639Z digest=sha256:d4a153ece6f9824746f0c931ce5431e3fe0bcf0bdeda0daee0f3929657f50845

Observation 45e6af44-ff99-474b-a8f8-de7f41a4402d · outbound

This paper cites Rehearsal-free Federated Domain-incremental Learning.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Rehearsal-free Federated Domain-incremental Learning

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-08-15T15:46:21.812781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T15:46:21.681524Z digest=sha256:61da573d0897824d011ed205e2456b43ac1247812360fc14e41e1e2aa1bd5079

Observation 94261611-37af-4a8f-b9f5-98b9c711b5ae · outbound

This paper cites Accurate Forgetting for Heterogeneous Federated Continual Learning.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Accurate Forgetting for Heterogeneous Federated Continual Learning

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.695913Z digest=sha256:4a5a2eef1b3d10b1a8eb4026fd350a98499bf11efa0876abf1908d7e9ac7a284

Observation 23621060-4092-4fa6-8537-c752e8c76b93 · outbound

This paper cites an unresolved cited work.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Unresolved cited work

Reference 19

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unresolved
raw_fallback, observed 2026-08-15T15:46:22.082494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T15:46:21.720606Z digest=sha256:34a2cf6584dfabe265f1c8c5ec5b742094d46e5f6f154ebb5f840afc8718463c

Observation 3098b1fd-902c-450d-ba53-6a1bf08f37b5 · outbound

This paper cites CLIENT-VS. SERVER-SIDEPROXIMALTERMS.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer CLIENT-VS. SERVER-SIDEPROXIMALTERMS

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-15T15:46:22.067904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T15:46:21.725143Z digest=sha256:38d5b1216f5598c0b1edfea821c2a2ae9da7abd3cc7c3f051ee7ea989b2a798c

Observation a1038bce-04a8-4705-bd47-0e6bff05cf57 · outbound

This paper cites A distillation-based approach integrating continual learning and federated learning for pervasive services.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer A distillation-based approach integrating continual learning and federated learning for pervasive services

Reference 2011

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.691185Z digest=sha256:4222c637ee3bc68a9c0716adc8ad43aa93b409322f6a821ad33eba952be4bb4f

Observation 02db74b9-dad8-4c0e-8499-be0e3e3e7912 · outbound

This paper cites Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning

Reference 2014

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no resolver link, observed 2026-08-15T15:46:21.705115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.705115Z digest=sha256:36cc9f487cfa02438508d6311f47e646f5f582df149add79fcce31438f864c03

Observation fb789b51-bbd3-459d-9667-add8b00d637a · outbound

This paper cites Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning

Reference 2017

Resolution
verified exact
local_arxiv, observed 2026-08-15T15:46:22.001425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T15:46:21.646495Z digest=sha256:0703b358f9392c002b91cf5859d3a971524838810bf6f3b5b7747781ea44138a

Observation d867fad0-7869-4094-b316-e89d7d4bf3a6 · outbound

This paper cites Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory

Reference 2018

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.636179Z digest=sha256:08a1333f8504ff3b3bf68910e2f9457ee779ab8b00da913c35ff9799af5f33a6

Observation eec38035-3dc3-4c75-8a98-21ba6352d3ea · outbound

This paper cites On the convergence of continual federated learning using incrementally aggregated gradients.arXiv preprint arXiv:2411.07959,.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer On the convergence of continual federated learning using incrementally aggregated gradients.arXiv preprint arXiv:2411.07959,

Reference 2019

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.651799Z digest=sha256:f8adc28ec735f66d215d53b45d5c38412df82f963510e8f0a42670bb0d9e9d4e

Observation 50dbbe89-d93e-4d08-ba23-eca449baedb3 · outbound

This paper cites On the Convergence of FedAvg on Non-IID Data.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer On the Convergence of FedAvg on Non-IID Data

Reference 2020

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no resolver link, observed 2026-08-15T15:46:21.656648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.656648Z digest=sha256:4a03b5587ab7a60d5d4592ed3a0376ba04ee19ec54ace522b12101fb15eec36d

Observation d404cc1d-3669-48d3-9df6-7ad0cd0b3ae0 · outbound

This paper cites However, most FCL methods lack theoretical guarantees.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer However, most FCL methods lack theoretical guarantees

Reference 2021

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verified fuzzy
raw_fallback, observed 2026-08-15T15:46:22.096786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T15:46:21.714524Z digest=sha256:6458e90d902818cb26a7cda70357e13ac0f1523ae30cd27fd953a538dafeb5f5

Observation 093d9b50-1b94-453e-8ec8-809eec982650 · outbound

This paper cites an unresolved cited work.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Unresolved cited work

Reference 2022

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unresolved
raw_fallback, observed 2026-08-15T15:46:22.111750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 7ce6cbf7-4f72-42b1-b2e7-61b7eae8678f · outbound

This paper cites Local SGD Converges Fast and Communicates Little.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Local SGD Converges Fast and Communicates Little

Reference 2023

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:46:21.676342Z digest=sha256:f1e4b5449ae0cc7c2e448599827829f960457f43eed77d0471c4f661a3bd8941

Observation 8962e591-f574-43b4-9b98-4f8edaf4fb0e · outbound

This paper cites Unbiased look at dataset bias.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Unbiased look at dataset bias

Reference 2024

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verified fuzzy
raw_fallback, observed 2026-08-15T15:46:22.141403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation c2681ab9-6c67-46d3-8ba1-422a59269018 · outbound

This paper cites A proximal stochastic gradient method with progressive variance reduc- tion.SIAM Journal on Optimization, 24(4):2057–2075,.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer A proximal stochastic gradient method with progressive variance reduc- tion.SIAM Journal on Optimization, 24(4):2057–2075,

Reference 2025

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verified fuzzy
raw_fallback, observed 2026-08-15T15:46:22.125969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Pith citing papers

No inbound Pith citation observations are available.