Paper Citation Record · LEDGER

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient

As of 13 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2412.00126.

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

pith.paper-citation-record.v1
2412.00126 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:35:58.320218Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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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

47 of 47 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dd67d26b-4237-412b-ae6d-68b07c7ed2a7 · outbound

This paper cites Scu: An efficient machine unlearning scheme for deep learning enabled semantic communications,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Scu: An efficient machine unlearning scheme for deep learning enabled semantic communications,

Reference 1

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Observation 101cfa2b-7780-43e7-b9b7-b5cbb308bd29 · outbound

This paper cites Zero- shot machine unlearning,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Zero- shot machine unlearning,

Reference 2

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Observation fc415a67-048b-45e0-a282-88d220086f40 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Communication-efficient learning of deep networks from decentralized data,

Reference 3

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Observation 53da5d6d-494c-47b1-8842-f68a515359b2 · outbound

This paper cites General data protection regulation (gdpr),.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient General data protection regulation (gdpr),

Reference 4

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Observation 4ddda57a-7bb7-45db-b468-b687f05da43d · outbound

This paper cites Klein, Canadian Privacy: Data Protection and Policy for the Practi- tioner.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Klein, Canadian Privacy: Data Protection and Policy for the Practi- tioner

Reference 5

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Observation bd8729e8-5bd8-400e-831b-a2f23708e4be · outbound

This paper cites A survey on federated unlearning: Challenges, methods, and future directions,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient A survey on federated unlearning: Challenges, methods, and future directions,

Reference 6

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Observation 75db9206-80f6-4b73-bd28-78507f8b79f8 · outbound

This paper cites Machine unlearning,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Machine unlearning,

Reference 7

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Observation 614f9c83-a82e-469f-8c4c-c80832eb851c · outbound

This paper cites Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher,

Reference 8

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Observation 13b8ca1d-c8af-4a3e-95b8-85aa85de6d50 · outbound

This paper cites Machine unlearning via rep- resentation forgetting with parameter self-sharing,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Machine unlearning via rep- resentation forgetting with parameter self-sharing,

Reference 9

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Observation 1a5a5db1-876e-4b83-9619-e0b09933f246 · outbound

This paper cites Forgetting and remembering are both you need: Balanced graph structure unlearning,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Forgetting and remembering are both you need: Balanced graph structure unlearning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:35:58.661995Z

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Observation d17b2697-b94b-444d-8d89-cbf833a9ce74 · outbound

This paper cites A Survey on Federated Unlearning: Challenges and Opportunities.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient A Survey on Federated Unlearning: Challenges and Opportunities

Reference 11

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Observation d8f77efe-116d-4ada-9799-216281ed2041 · outbound

This paper cites Fedrecover: Recovering from poisoning attacks in federated learning using historical information,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Fedrecover: Recovering from poisoning attacks in federated learning using historical information,

Reference 12

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Observation e3562e57-9057-46ea-825c-86ce60536f96 · outbound

This paper cites Revfrf: Enabling cross-domain random forest training with revocable federated learning,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Revfrf: Enabling cross-domain random forest training with revocable federated learning,

Reference 13

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Observation f738f22f-f503-4d97-b13c-9c6652d4fa40 · outbound

This paper cites Mit- igating poor data quality impact with federated unlearning for human- centric metaverse,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Mit- igating poor data quality impact with federated unlearning for human- centric metaverse,

Reference 14

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Observation cb222768-8a88-4b11-9de7-e75606af41ec · outbound

This paper cites Unlearning backdoor attacks in federated learning,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Unlearning backdoor attacks in federated learning,

Reference 15

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Observation 12528e36-f178-440f-8450-db659c13f7fa · outbound

This paper cites Fedrecov- ery: Differentially private machine unlearning for federated learning frameworks,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Fedrecov- ery: Differentially private machine unlearning for federated learning frameworks,

Reference 16

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Observation 28cd1e75-3b50-45dd-b50d-d8511018da16 · outbound

This paper cites Forgettable Federated Linear Learning with Certified Data Unlearning.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Forgettable Federated Linear Learning with Certified Data Unlearning

Reference 17

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Observation 2dc230ab-b625-40e0-b780-c085f5aed6db · outbound

This paper cites The right to be forgotten in federated learning: An efficient realization with rapid retraining,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient The right to be forgotten in federated learning: An efficient realization with rapid retraining,

Reference 18

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Observation 31f77ea2-652f-4d3c-8bfb-ade1a1e8ed84 · outbound

This paper cites Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation

Reference 19

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Observation 3e575b04-96c2-4fe8-ba4d-6815cde3ae7c · outbound

This paper cites QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation

Reference 20

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Observation f9338e3a-f8b7-49f8-83bb-a796a2f6fca5 · outbound

This paper cites Forget-svgd: Particle- based bayesian federated unlearning,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Forget-svgd: Particle- based bayesian federated unlearning,

Reference 21

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Observation c2402038-007e-4142-8091-0dd4842ebf15 · outbound

This paper cites Compressed particle-based feder- ated bayesian learning and unlearning,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Compressed particle-based feder- ated bayesian learning and unlearning,

Reference 22

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Observation 1491873b-7292-49f3-b2cd-eb8b0c36e11f · outbound

This paper cites Fedharmony: Unlearning scanner bias with distributed data,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Fedharmony: Unlearning scanner bias with distributed data,

Reference 23

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Observation 00cbd13a-e8c8-46b9-9796-1d326325071e · outbound

This paper cites Bfu: Bayesian federated unlearning with parameter self-sharing,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Bfu: Bayesian federated unlearning with parameter self-sharing,

Reference 24

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Observation 62591b76-ac0e-4694-8b98-816a7ee44c48 · outbound

This paper cites Heterogeneous federated knowledge graph embedding learning and unlearning,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Heterogeneous federated knowledge graph embedding learning and unlearning,

Reference 25

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Observation f884a821-a902-43a5-ae5b-5fe6dc7d8fe9 · outbound

This paper cites Asynchronous federated unlearning,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Asynchronous federated unlearning,

Reference 26

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This paper cites Federaser: Enabling efficient client-level data removal from federated learning models,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Federaser: Enabling efficient client-level data removal from federated learning models,

Reference 27

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Observation b130dee9-a3a9-4762-b84d-262d9f938404 · outbound

This paper cites Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning

Reference 28

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Observation 25c469d5-958e-49d1-be95-39573c52db38 · outbound

This paper cites Communication Efficient and Provable Federated Unlearning.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Communication Efficient and Provable Federated Unlearning

Reference 29

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Observation 7d3d6641-e801-40cb-a6a9-3b5d5d0c1b53 · outbound

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Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Federated unlearning for on-device recommendation,

Reference 30

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Observation d94529a0-02bf-4dbc-aeaf-fd28d33bc414 · outbound

This paper cites Fast federated machine unlearning with nonlinear functional theory,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Fast federated machine unlearning with nonlinear functional theory,

Reference 31

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Streamlined Federated Unlearning: Unite as One to Be Highly Efficient A revocation key- based approach towards efficient federated unlearning,

Reference 32

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This paper cites Edge caching with federated unlearning for low-latency v2x communications,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Edge caching with federated unlearning for low-latency v2x communications,

Reference 33

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Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Verifi: Towards verifiable federated unlearning,

Reference 34

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This paper cites Federated unlearning with momentum degradation,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Federated unlearning with momentum degradation,

Reference 35

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This paper cites Federated unlearning via class- discriminative pruning,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Federated unlearning via class- discriminative pruning,

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This paper cites Federated Unlearning: How to Efficiently Erase a Client in FL?.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 37

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This paper cites Subspace based Federated Unlearning.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Subspace based Federated Unlearning

Reference 38

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This paper cites Federated unlearning: Guarantee the right of clients to forget,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Federated unlearning: Guarantee the right of clients to forget,

Reference 39

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Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Fedu: Federated unlearning via user-side influence approximation forgetting,

Reference 40

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This paper cites Spfl: A self-purified federated learning method against poisoning attacks,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Spfl: A self-purified federated learning method against poisoning attacks,

Reference 41

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This paper cites Personal- ized edge intelligence via federated self-knowledge distillation,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Personal- ized edge intelligence via federated self-knowledge distillation,

Reference 42

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Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Learning multiple layers of features from tiny images,

Reference 43

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This paper cites Character-level convolutional net- works for text classification,.

Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Character-level convolutional net- works for text classification,

Reference 44

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Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Deep residual learning for image recognition,

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Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Long short-term memory,

Reference 46

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Streamlined Federated Unlearning: Unite as One to Be Highly Efficient Bayesian nonparametric federated learning of neural networks,

Reference 47

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Pith citing papers

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