Paper Citation Record · LEDGER

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity

As of 22 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2504.17520.

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

pith.paper-citation-record.v1
2504.17520 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:45:00.464443Z

measured 43 of 43 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

43 of 43 outbound references displayed

verified exact1
verified fuzzy38
unresolved4
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3742700b-bb4b-40af-bc28-20379c6550dc · outbound

This paper cites Differential ly private federated clustering over non-iid data,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Differential ly private federated clustering over non-iid data,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.208403Z

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 fe7dfc43-72f2-4a56-9eff-0aa5cd51a56e · outbound

This paper cites Distributed Multi-View Sparse V ector Recovery,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Distributed Multi-View Sparse V ector Recovery,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.191509Z

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 3274f754-4726-40f8-a457-f6f6bd6356e4 · outbound

This paper cites Distributed Learning Over Networks With Graph-Attention-Based Person al- ization,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Distributed Learning Over Networks With Graph-Attention-Based Person al- ization,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.173239Z

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 ac088f78-4439-44e9-ba07-0c4568fddb76 · outbound

This paper cites Can decentralized algorithms outperform centrali zed algorithms? A case study for decentralized parallel stocha stic gradient descent,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Can decentralized algorithms outperform centrali zed algorithms? A case study for decentralized parallel stocha stic gradient descent,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.155300Z

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 b89e6cd5-9577-48d9-b098-432b3fceb3f1 · outbound

This paper cites Personalize d federated learning with theoretical guarantees: A model-a gnostic meta-learning approach,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Personalize d federated learning with theoretical guarantees: A model-a gnostic meta-learning approach,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.138797Z

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 f1f9f144-592d-42e7-836c-e3d1920675bb · outbound

This paper cites Cluster-driven graph federated learning ov er multiple domains.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Cluster-driven graph federated learning ov er multiple domains

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.122876Z

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 17137210-2274-4a9c-94ff-0e0d885b9733 · outbound

This paper cites Federated optimization in heterogeneous networ ks,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Federated optimization in heterogeneous networ ks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.105514Z

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 3fb22eda-a91e-4bc0-aade-74d627cf5fb6 · outbound

This paper cites An efﬁcie nt framework for clustered federated learning,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity An efﬁcie nt framework for clustered federated learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.089059Z

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 f0ad0e70-d28d-480b-b352-740ad0dbe5e1 · outbound

This paper cites Personalized federat ed learning with moreau envelopes,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Personalized federat ed learning with moreau envelopes,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.073226Z

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 49397b03-6b54-4c87-b56f-3c4952181ce4 · outbound

This paper cites Group knowledge transfer: Federated learning of large cnns at the edge,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Group knowledge transfer: Federated learning of large cnns at the edge,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.057885Z

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 6ef175f2-aee7-4721-a7bd-73e54f936a27 · outbound

This paper cites Distributed learning of deep ne ural network over multiple agents,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Distributed learning of deep ne ural network over multiple agents,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.041074Z

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 3a157ca3-0c6d-4c6e-a29f-24816950c5eb · outbound

This paper cites HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T10:45:00.321411Z

Source-reported events for the cited work

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Observation cd0a3ef8-6ff5-4fdf-91bc-83cc12bfaabf · outbound

This paper cites Fjord: Fair and accurate federated lear ning under heterogeneous targets with ordered dropout,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Fjord: Fair and accurate federated lear ning under heterogeneous targets with ordered dropout,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.023772Z

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 941103fc-ced8-4181-bc1b-3733e2e1ddb3 · outbound

This paper cites Fjord: Fair and accurate federated learning under heterog eneous targets with ordered dropout,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Fjord: Fair and accurate federated learning under heterog eneous targets with ordered dropout,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:01.007296Z

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 5279db65-f9ce-4996-b09c-f2e35b9aef0e · outbound

This paper cites LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T10:45:00.335087Z

Source-reported events for the cited work

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Observation 07f76024-bbcd-4b47-9a1e-d24c635e2c1b · outbound

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Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-16T10:45:00.990845Z

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 069e8770-ad9b-4af1-a50a-01a722d29b19 · outbound

This paper cites Model pruning enables efﬁcient federate d learning on edge devices,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Model pruning enables efﬁcient federate d learning on edge devices,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.975152Z

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 a17f1608-40ba-4162-9107-0b1fe15d2106 · outbound

This paper cites FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T10:45:00.348542Z

Source-reported events for the cited work

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Observation f37b6b9d-c8e2-4490-9f7b-8bd0611921d6 · outbound

This paper cites Federated Learning of Large Models at the Edge via Principal Sub-Model Training.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Federated Learning of Large Models at the Edge via Principal Sub-Model Training

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-16T10:45:00.506791Z

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 ddd13d3c-bf40-40f4-a663-91f94189b94c · outbound

This paper cites Resource-adaptive federated learning with all-in-one neural composition,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Resource-adaptive federated learning with all-in-one neural composition,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.960625Z

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 3f9adcc0-384b-4242-8e99-c602f8c49584 · outbound

This paper cites Fully decent ralized joint learning of personalized models and collaboration gr aphs,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Fully decent ralized joint learning of personalized models and collaboration gr aphs,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.945072Z

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 19811ca3-fbaa-45e9-a2e9-f1df90813392 · outbound

This paper cites Enhancing Decentralized and Personalized Federated Learning with To pol- ogy Construction,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Enhancing Decentralized and Personalized Federated Learning with To pol- ogy Construction,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.928098Z

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 39800069-ab24-4cb9-a4c5-34f057cf8a1c · outbound

This paper cites DePRL: Achiev- ing Linear Convergence Speedup in Personalized Decentrali zed Learning with Shared Representations,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity DePRL: Achiev- ing Linear Convergence Speedup in Personalized Decentrali zed Learning with Shared Representations,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.910773Z

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 3a421bd9-cb9d-409e-a7be-58ee7aad02ce · outbound

This paper cites Dispﬂ: Towards communication-efﬁcient personalized federated learning via de- centralized sparse training,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Dispﬂ: Towards communication-efﬁcient personalized federated learning via de- centralized sparse training,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.889887Z

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 7b1b9699-677f-407f-ae72-d9d8e251b4b2 · outbound

This paper cites Dlion: Decentralized distribu ted deep learning in micro-clouds,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Dlion: Decentralized distribu ted deep learning in micro-clouds,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.872297Z

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 5593758d-f740-4e7f-b04f-a93c80bb34a1 · outbound

This paper cites Mitigating stragglers i n the decentralized training on heterogeneous clusters,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Mitigating stragglers i n the decentralized training on heterogeneous clusters,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.854746Z

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 2018bfa5-93cc-497b-9ae4-59afe35072ba · outbound

This paper cites Federated learni ng via over-the-air computation,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Federated learni ng via over-the-air computation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.839979Z

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 6cd36656-2829-4be0-8172-aaecca9c7c02 · outbound

This paper cites Adaptive federated learning in resource constrained edge computing systems,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Adaptive federated learning in resource constrained edge computing systems,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.825384Z

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 a9f33cd3-d9d6-4465-b9ca-43412bac9280 · outbound

This paper cites Communication-efﬁcie nt fed- erated learning based on compressed sensing,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Communication-efﬁcie nt fed- erated learning based on compressed sensing,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.809445Z

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 746cd140-0f2f-42ba-9007-bace5fcaa61b · outbound

This paper cites Privacy-preserving federated primal-dual learning for n on- convex and non-smooth problems with model sparsiﬁcation,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Privacy-preserving federated primal-dual learning for n on- convex and non-smooth problems with model sparsiﬁcation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.793694Z

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 8274d6ff-9566-4aba-92ec-28b6663df62b · outbound

This paper cites Nonlinear perturbation-based non-convex optimiza tion over time-varying networks,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Nonlinear perturbation-based non-convex optimiza tion over time-varying networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.778414Z

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 90d7db2e-cce4-4af0-a201-dfd086505022 · outbound

This paper cites Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learnin g from Distributed Data,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learnin g from Distributed Data,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.762730Z

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 387b255e-1f84-4e73-98e2-fcd05af7d3d6 · outbound

This paper cites Fedmas k: Joint computation and communication-efﬁcient personaliz ed fed- erated learning via heterogeneous masking,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Fedmas k: Joint computation and communication-efﬁcient personaliz ed fed- erated learning via heterogeneous masking,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.747726Z

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 841d3cd9-6ad3-4601-a20b-67ba7a050bd2 · outbound

This paper cites A lightweight an d secure deep learning model for privacy-preserving federated lear ning in intelligent enterprises,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity A lightweight an d secure deep learning model for privacy-preserving federated lear ning in intelligent enterprises,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.730032Z

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 48d9bc06-4c86-4ca7-a1e1-cd4ac7980fd8 · outbound

This paper cites Decentralized a nd robust privacy-preserving model using blockchain-enabled feder ated deep learning in intelligent enterprises,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Decentralized a nd robust privacy-preserving model using blockchain-enabled feder ated deep learning in intelligent enterprises,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.713847Z

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 d6025daf-ac5e-44c6-a1b4-aae889de0cca · outbound

This paper cites Deconstructing lottery tickets: Zeros, signs, and the supermask,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Deconstructing lottery tickets: Zeros, signs, and the supermask,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.697904Z

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 7de2c797-46f5-4215-998c-bfb6beee6b78 · outbound

This paper cites Proving the lottery ticket hypothesis: Pruning is all you n eed,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Proving the lottery ticket hypothesis: Pruning is all you n eed,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.681609Z

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 564dd627-f186-4c80-80b8-3f9ee8c4b5c1 · outbound

This paper cites The Lottery Ticket Hypothes is: Finding Sparse, Trainable Neural Networks,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity The Lottery Ticket Hypothes is: Finding Sparse, Trainable Neural Networks,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.665584Z

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 d537f98a-0d82-4971-9d20-fdca916d3949 · outbound

This paper cites What’s hidden in a randomly weighted neural network?,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity What’s hidden in a randomly weighted neural network?,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.648506Z

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 779a771b-a0f9-4d02-844f-7161053d34a2 · outbound

This paper cites Optimal lottery tickets via subset sum: Logarit hmic over-parameterization is sufﬁcient,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Optimal lottery tickets via subset sum: Logarit hmic over-parameterization is sufﬁcient,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.630261Z

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 33e2125a-2e18-44cb-9710-b7d7a6fda912 · outbound

This paper cites Proving the stro ng lottery ticket hypothesis for convolutional neural networ ks,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Proving the stro ng lottery ticket hypothesis for convolutional neural networ ks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.612284Z

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 0c26851d-6a6b-4ff7-9638-4a2358f1d888 · outbound

This paper cites Group sparse regularization for deep neural networks,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Group sparse regularization for deep neural networks,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.593098Z

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 03ac337e-e536-4c4d-9724-dd7449860a36 · outbound

This paper cites Hierarchical grou p sparse regularization for deep convolutional neural networks,.

Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Hierarchical grou p sparse regularization for deep convolutional neural networks,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:45:00.576408Z

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.