Paper Citation Record · LEDGER

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions

As of 20 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2505.15579.

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

pith.paper-citation-record.v1
2505.15579 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:21:58.335796Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

15 of 15 outbound references displayed

verified exact1
verified fuzzy6
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 20bcf3ed-de00-458a-be58-dcc9321f1101 · outbound

This paper cites Improving Federated Learning Personalization via Model Agnostic Meta Learning.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions Improving Federated Learning Personalization via Model Agnostic Meta Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:58.256225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e2b1fda-f970-46d1-b360-1d182824253f · outbound

This paper cites From Low Intrinsic Dimensionality to Non-Vacuous Generalization Bounds in Deep Multi-Task Learning.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions From Low Intrinsic Dimensionality to Non-Vacuous Generalization Bounds in Deep Multi-Task Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:58.289655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3ae8cfe-c3ad-4fc1-8ed8-a49576c459c2 · outbound

This paper cites we have a distribution D, and the goal is to generalize from training data to the unseen data.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions we have a distribution D, and the goal is to generalize from training data to the unseen data

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:58.714601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 683a7fff-98c3-40ab-94cb-f783b4f871b8 · outbound

This paper cites Parameter decomposition-approaches (Arivazhagan et al., 2019; Collins et al., 2021; Marfoq et al., 2022; Chen et al., 2023; Wu et al.,.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions Parameter decomposition-approaches (Arivazhagan et al., 2019; Collins et al., 2021; Marfoq et al., 2022; Chen et al., 2023; Wu et al.,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:58.655870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7d38b384-062c-468e-a2bb-4ab7b7aa9f83 · outbound

This paper cites Federated multi-task approaches (Smith et al., 2017; Dinh et al., 2020; Hanzely et al., 2020; Marfoq et al., 2021; Li et al., 2021; Lin et al., 2022; Ye et al., 2023; Zhang et al.,.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions Federated multi-task approaches (Smith et al., 2017; Dinh et al., 2020; Hanzely et al., 2020; Marfoq et al., 2021; Li et al., 2021; Lin et al., 2022; Ye et al., 2023; Zhang et al.,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:58.637140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3187630c-3fdc-4538-b29c-5f423de28c4e · outbound

This paper cites All of the above approaches, however, require a client to have labeled data in order to obtain a personalized model.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions All of the above approaches, however, require a client to have labeled data in order to obtain a personalized model

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:58.617094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 47c1cf87-81e8-4b0d-8d7f-34f0588db62f · outbound

This paper cites an unresolved cited work.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions Unresolved cited work

Reference 256

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:21:58.674967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7cd3859c-009f-447c-8740-14304e9f3b09 · outbound

This paper cites For all methods we tune the local learning rate ηl on validation clients.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions For all methods we tune the local learning rate ηl on validation clients

Reference 500

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:58.694004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0e8628c4-e175-40ad-b57b-696d303af5cc · outbound

This paper cites Maurer, A.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions Maurer, A

Reference 2004

Resolution: verified exact; raw_fallback, observed 2026-08-07T15:21:58.514559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d72e80ed-6648-4454-bf9c-1243e4c0dd4d · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:58.276318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40a6c29a-a711-4c79-ad40-0a60a0cb95bf · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions LEAF: A Benchmark for Federated Settings

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:58.242251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb507a23-fe5e-497a-a65f-5daebd455102 · outbound

This paper cites Federated Learning with Personalization Layers.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions Federated Learning with Personalization Layers

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:58.233966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 811bb885-22d9-4a8b-ae52-e2f79ee43c8d · outbound

This paper cites Federated Unsupervised Representation Learning.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions Federated Unsupervised Representation Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:58.297379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4102585d-f249-44c5-b469-5a1929002de0 · outbound

This paper cites UPFL: unsupervised personalized federated learning towards new clients.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions UPFL: unsupervised personalized federated learning towards new clients

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:58.734575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3dec3547-3c63-411c-8cdb-dee20fece73f · outbound

This paper cites pfl-research: simulation framework for accelerating research in Private Federated Learning.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions pfl-research: simulation framework for accelerating research in Private Federated Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:58.248378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.