Paper Citation Record · LEDGER

Federated Continual Learning for Edge-AI: A Comprehensive Survey

As of 20 August 2026, this Paper Citation Record lists 100 of 170 outbound references and 7 inbound Pith citation observations for arXiv:2411.13740.

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

pith.paper-citation-record.v1
2411.13740 v1

Coverage vector

measured 100 of 170 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:59:30.279707Z

measured 107 of 107 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:46:50.838046Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T03:57:38.780121Z

Reference resolution

100 of 170 outbound references displayed

verified exact7
verified fuzzy0
unresolved93
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 51b0b486-0a7e-4256-8ba5-83ca80e4284c · outbound

This paper cites A survey on deep learning and its applications.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A survey on deep learning and its applications

Reference 1

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Observation 9a76a6a2-bead-4834-be1a-090cb880ba34 · outbound

This paper cites A survey of blockchain and artificial intelligence for 6g wireless communications.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A survey of blockchain and artificial intelligence for 6g wireless communications

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.863191Z

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Observation acb8d6b0-2199-4ab9-932f-93fd27aa975a · outbound

This paper cites Deep learning-enabled medical computer vision.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Deep learning-enabled medical computer vision

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.867844Z

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Observation b5c7121d-0058-4d0b-b858-e820612cdd46 · outbound

This paper cites A survey of deep learning applications to autonomous vehicle control.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A survey of deep learning applications to autonomous vehicle control

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.872349Z

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Observation 5d6929c9-512f-4536-a346-22caa2bdbba7 · outbound

This paper cites Current progress and open challenges for applying deep learning across the biosciences.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Current progress and open challenges for applying deep learning across the biosciences

Reference 5

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Observation d6e451c6-45f1-46f5-8116-ade501324edf · outbound

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

Federated Continual Learning for Edge-AI: A Comprehensive Survey Communication-efficient learning of deep networks from decentralized data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.881554Z

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Observation 58369609-57d7-4906-9011-6995e3df989b · outbound

This paper cites A survey on federated learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A survey on federated learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.886361Z

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Observation 5d43ac09-9257-4dc6-a631-58989b831c75 · outbound

This paper cites The future of digital health with federated learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey The future of digital health with federated learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.890531Z

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Observation 2c8b1d5e-7bd6-4f8e-9015-5393e737d3e5 · outbound

This paper cites Federated learning on non-iid data: A survey.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated learning on non-iid data: A survey

Reference 9

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Observation df753fdb-59d7-49a6-9761-6e4c0c30d722 · outbound

This paper cites Model aggregation techniques in federated learning: A comprehensive survey.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Model aggregation techniques in federated learning: A comprehensive survey

Reference 10

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Observation 3b9adfca-5054-42ab-a97a-aa1b8ce020cc · outbound

This paper cites A survey on security and privacy of federated learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A survey on security and privacy of federated learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.903217Z

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Observation 65eb8fe6-e168-4ba8-af6e-185fa607b46d · outbound

This paper cites Federated learning for edge networks: Resource optimization and incentive mechanism.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated learning for edge networks: Resource optimization and incentive mechanism

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.907181Z

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Observation 7234fdb8-41cd-44c2-85ee-494364c9ffee · outbound

This paper cites A comprehensive survey of continual learning: Theory, method and application.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A comprehensive survey of continual learning: Theory, method and application

Reference 13

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Observation b69c7d69-fa51-42d7-a21d-a89230aba45e · outbound

This paper cites Non-iid data and continual learning processes in federated learning: A long road ahead.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Non-iid data and continual learning processes in federated learning: A long road ahead

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.915483Z

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Observation 494d9207-dbfa-4aaf-8e3e-299321bd11be · outbound

This paper cites Continual federated learning based on knowledge distillation.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Continual federated learning based on knowledge distillation

Reference 15

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Observation 23664367-f292-4063-9d45-b26c4173b0d8 · outbound

This paper cites Federated class-incremental learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated class-incremental learning

Reference 16

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Observation 2c6a1f8d-26a7-439a-8efa-edb218d72118 · outbound

This paper cites Target: Federated class-continual learning via exemplar- free distillation.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Target: Federated class-continual learning via exemplar- free distillation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.927747Z

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Observation 7e8b9420-dc23-4d37-9dcc-93d016a925df · outbound

This paper cites Asynchronous federated continual learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Asynchronous federated continual learning

Reference 18

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Observation b3e370d0-3c2f-4b3d-8833-01fe7af4b7fb · outbound

This paper cites Federated learning for the internet of things: Applications, challenges, and opportunities.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated learning for the internet of things: Applications, challenges, and opportunities

Reference 19

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Observation ba9f479a-e4b8-4829-931a-ca337abc8bf3 · outbound

This paper cites Heterogeneous federated learning: State-of-the-art and research challenges.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Heterogeneous federated learning: State-of-the-art and research challenges

Reference 20

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Observation 7d7acb88-b1b6-482e-a873-a1e2f5c7a51e · outbound

This paper cites Three types of incremental learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Three types of incremental learning

Reference 21

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Observation f36690ab-7471-4f5b-9f64-a953bc52cbd8 · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A continual learning survey: Defying forgetting in classification tasks

Reference 22

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Observation abc0545f-c60a-4ca7-92b7-11ec13cd9bcc · outbound

This paper cites Class-incremental learning: survey and performance evaluation on image classification.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Class-incremental learning: survey and performance evaluation on image classification

Reference 23

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Observation d8a20e3d-53d6-4521-bd24-c318df008771 · outbound

This paper cites Federated Continual Learning via Knowledge Fusion: A Survey.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated Continual Learning via Knowledge Fusion: A Survey

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:59:30.995415Z

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Observation e06006da-b813-46d2-8e4c-28af66513679 · outbound

This paper cites Deep class-incremental learning: A survey, February 2023.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Deep class-incremental learning: A survey, February 2023

Reference 25

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Observation 6894858d-70e2-4d5f-a897-b6f970b49d03 · outbound

This paper cites Federated Learning for Data Streams.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated Learning for Data Streams

Reference 26

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Observation de5c72b6-c4a1-463b-b4ea-f4be45e3d4fa · outbound

This paper cites Hendryx, Dharma Raj KC, Bradley Walls, and Clayton T.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Hendryx, Dharma Raj KC, Bradley Walls, and Clayton T

Reference 27

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Observation 2b5cc5bf-5462-4eb8-b7d4-5f9fffcaa239 · outbound

This paper cites Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio

Reference 28

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Observation 8b8eab44-7965-4c97-9404-2857f498004a · outbound

This paper cites Better generative replay for continual federated learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Better generative replay for continual federated learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:29.978374Z

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Observation b3702934-cccd-4cd9-9f0a-26771c0e705b · outbound

This paper cites Don’t memorize; mimic the past: Federated class incremental learning without episodic memory.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Don’t memorize; mimic the past: Federated class incremental learning without episodic memory

Reference 30

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Observation 6dc80dc2-f203-413d-b06b-73917ccd4bda · outbound

This paper cites A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks

Reference 31

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Observation ae07be6f-863c-49cc-b793-908c38f43871 · outbound

This paper cites No One Left Behind: Real-World Federated Class-Incremental Learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey No One Left Behind: Real-World Federated Class-Incremental Learning

Reference 32

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Observation ff83d797-13a3-4e56-88e9-62a9729dee99 · outbound

This paper cites Federated incremental semantic segmentation.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated incremental semantic segmentation

Reference 33

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Observation 2986921c-d8bd-4f36-9e46-984836eaef1f · outbound

This paper cites Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:59:30.955429Z

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Observation 70aad190-0866-440a-a938-20206076b987 · outbound

This paper cites A federated incremental learning algorithm based on dual attention mechanism.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A federated incremental learning algorithm based on dual attention mechanism

Reference 35

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Observation 4d41ae8b-b592-4143-a9a4-ce60777d2e7e · outbound

This paper cites an unresolved cited work.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Unresolved cited work

Reference 36

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Observation 048edbda-6f96-4310-90b2-7adbb7b408d8 · outbound

This paper cites Continual local training for better initialization of federated models.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Continual local training for better initialization of federated models

Reference 37

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Observation 509dd77c-1844-4054-a634-6187ccb1ba71 · outbound

This paper cites Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell

Reference 38

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Observation e83e2f89-e8be-4a89-9830-deda86ee7a26 · outbound

This paper cites Scalable and order-robust continual learning with additive parameter decomposition.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Scalable and order-robust continual learning with additive parameter decomposition

Reference 39

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Observation f2f4db9c-739a-42fe-a3de-bb9d88d5a584 · outbound

This paper cites Federated continual learning with weighted inter-client transfer.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated continual learning with weighted inter-client transfer

Reference 40

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Observation 66102dd0-9a3a-42f2-8ef9-7ec743bd510c · outbound

This paper cites Cross-fcl: Toward a cross-edge federated continual learning framework in mobile edge computing systems.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Cross-fcl: Toward a cross-edge federated continual learning framework in mobile edge computing systems

Reference 41

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Observation 1870b1ae-29fe-4521-919a-0cbf621fd739 · outbound

This paper cites Fedknow: Federated continual learning with signature task knowledge integration at edge.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Fedknow: Federated continual learning with signature task knowledge integration at edge

Reference 42

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Observation bb5c5c28-172e-44b2-939a-6c869f5b0ca9 · outbound

This paper cites Continual Adaptation of Vision Transformers for Federated Learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Continual Adaptation of Vision Transformers for Federated Learning

Reference 43

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Observation 7ae4d6de-dc49-455f-bd61-9d313d690482 · outbound

This paper cites Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning, September 2023.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning, September 2023

Reference 44

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This paper cites Federated Class-Incremental Learning with Prompting.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated Class-Incremental Learning with Prompting

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:59:30.920964Z

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Observation b43ca7b6-67cf-48b4-bdae-ab11c010e401 · outbound

This paper cites Distilling the Knowledge in a Neural Network, March 2015.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Distilling the Knowledge in a Neural Network, March 2015

Reference 46

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Observation b1373124-ffde-4627-9b47-a2b22efe20c7 · outbound

This paper cites Learning without Forgetting.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Learning without Forgetting

Reference 47

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Observation caf1083c-6059-4228-b62a-accdbcb60c58 · outbound

This paper cites Federated continual learning through distillation in pervasive computing.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated continual learning through distillation in pervasive computing

Reference 48

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Observation 45d05268-b9b2-4043-8c82-a540e9d81809 · outbound

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

Federated Continual Learning for Edge-AI: A Comprehensive Survey A distillation-based approach integrating continual learning and federated learning for pervasive services

Reference 49

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Observation 52af3c53-5072-483a-b405-80a5d24b197c · outbound

This paper cites Knowledge lock: Overcoming catastrophic forgetting in federated learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Knowledge lock: Overcoming catastrophic forgetting in federated learning

Reference 50

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Observation 725eeb52-5007-4f15-898f-dc0ac9a58276 · outbound

This paper cites Fl-iids: A novel federated learning-based incremental intrusion detection system.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Fl-iids: A novel federated learning-based incremental intrusion detection system

Reference 51

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Observation 1e06c5d6-2df0-422d-b487-2fe66f2a6613 · outbound

This paper cites Fedet: a communication-efficient federated class- incremental learning framework based on enhanced transformer.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Fedet: a communication-efficient federated class- incremental learning framework based on enhanced transformer

Reference 52

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Observation 817e1ebb-6ff4-4f16-aab5-83f1d12660a9 · outbound

This paper cites Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:30.081112Z

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Observation be3637b9-691e-48cd-98c0-b6d28c6f9a0b · outbound

This paper cites Overcoming forgetting in local adaptation of federated learning model.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Overcoming forgetting in local adaptation of federated learning model

Reference 54

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Observation 268e6642-ddeb-44eb-99cb-0c79e79011dc · outbound

This paper cites Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:30.089280Z

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Observation 94de6f29-f291-4214-b729-8908bd0c0ef6 · outbound

This paper cites Federated and continual learning for classification tasks in a society of devices.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated and continual learning for classification tasks in a society of devices

Reference 56

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Observation 38b595aa-a8d7-46ea-80ce-1fb051fd567b · outbound

This paper cites Concept drift detection and adaptation for federated and continual learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Concept drift detection and adaptation for federated and continual learning

Reference 57

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Observation 45db4aa3-f362-4fcb-9c9b-6f7817bcbd34 · outbound

This paper cites Ensemble and continual federated learning for classification tasks.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Ensemble and continual federated learning for classification tasks

Reference 58

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Spatial-temporal federated learning for lifelong person re- identification on distributed edges

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Learn from others and be yourself in heterogeneous federated learning

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Multi-granularity fusion resource allocation algorithm based on dual- attention deep reinforcement learning and lifelong learning architecture in heterogeneous iiot

Reference 61

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Towards Federated Learning on Time-Evolving Heterogeneous Data

Reference 62

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Incremental unsupervised adversarial domain adaptation for federated learning in iot networks

Reference 63

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning

Reference 64

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Continual adaptation of federated reservoirs in pervasive environments

Reference 65

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Communication-efficient federated continual learning for distributed learning system with non-iid data

Reference 66

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Differentially private federated continual learning with heterogeneous cohort privacy

Reference 67

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Efl: Elastic federated learning on non-iid data

Reference 68

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Continual learning of dynamical systems with competitive federated reservoir computing

Reference 69

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Continual horizontal federated learning for heterogeneous data

Reference 70

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated continuous learning with broad network architecture

Reference 71

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Attention-based federated incremental learning for traffic classification in the internet of things

Reference 72

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Lightweight privacy-preserving federated incremental decision trees

Reference 73

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated domain generalization with generalization adjustment

Reference 74

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Learnings from federated learning in the real world

Reference 75

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Secure and efficient parameters aggregation protocol for federated incremental learning and its applications

Reference 76

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Semi-supervised federated learning on evolving data streams

Reference 77

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This paper cites Finding trustworthy neighbors: Graph aided federated learning for few-shot industrial fault diagnosis with data heterogeneity.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Finding trustworthy neighbors: Graph aided federated learning for few-shot industrial fault diagnosis with data heterogeneity

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning

Reference 79

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This paper cites A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions

Reference 80

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated Continual Learning for Text Classification via Selective Inter-client Transfer

Reference 81

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This paper cites Federated continual learning with differentially private data sharing.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated continual learning with differentially private data sharing

Reference 82

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated probability memory recall for federated continual learning

Reference 83

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Unresolved cited work

Reference 84

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Lifelong bandit optimization: no prior and no regret

Reference 85

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This paper cites Personalized federated continual learning for task-incremental biometrics.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Personalized federated continual learning for task-incremental biometrics

Reference 86

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This paper cites Masked Autoencoders are Efficient Continual Federated Learners.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Masked Autoencoders are Efficient Continual Federated Learners

Reference 87

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This paper cites A deep learning-based smart service model for context-aware intelligent transportation system.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A deep learning-based smart service model for context-aware intelligent transportation system

Reference 88

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This paper cites Decentralized federated learning for extended sensing in 6g connected vehicles.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Decentralized federated learning for extended sensing in 6g connected vehicles

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Peer-to-peer federated continual learning for naturalistic driving action recognition

Reference 90

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Icmfed: An incremental and cost-efficient mechanism of federated meta-learning for driver distraction detection

Reference 91

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This paper cites A federated learning and blockchain framework for physiological signal classification based on continual learning.

Federated Continual Learning for Edge-AI: A Comprehensive Survey A federated learning and blockchain framework for physiological signal classification based on continual learning

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification

Reference 93

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated learning empowered real-time medical data processing method for smart healthcare

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated incremental learning based evolvable intrusion detection system for zero-day attacks

Reference 95

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Surveilnet: A lightweight anomaly detection system for cooperative iot surveillance networks

Reference 96

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This paper cites Collaborative and incremental learning for modulation classification with heterogeneous local dataset in cognitive iot.

Federated Continual Learning for Edge-AI: A Comprehensive Survey Collaborative and incremental learning for modulation classification with heterogeneous local dataset in cognitive iot

Reference 97

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Asynchronous semi-supervised federated learning with provable convergence in edge computing

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Federated continuous learning based on stacked broad learning system assisted by digital twin networks: An incremental learning approach for ACM Comput

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Federated Continual Learning for Edge-AI: A Comprehensive Survey Failure-Sentient Composition For Swarm-Based Drone Services

Reference 100

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Pith citing papers

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey cites this paper.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Federated Continual Learning for Edge-AI: A Comprehensive Survey

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Federated Continual Learning: Concepts, Challenges, and Solutions cites this paper.

Federated Continual Learning: Concepts, Challenges, and Solutions Federated Continual Learning for Edge-AI: A Comprehensive Survey

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Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Federated Continual Learning for Edge-AI: A Comprehensive Survey

Reference 13

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FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning cites this paper.

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning Federated Continual Learning for Edge-AI: A Comprehensive Survey

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PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks cites this paper.

PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks Federated Continual Learning for Edge-AI: A Comprehensive Survey

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PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks cites this paper.

PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks Federated Continual Learning for Edge-AI: A Comprehensive Survey

Reference 18

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Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data cites this paper.

Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data Federated Continual Learning for Edge-AI: A Comprehensive Survey

Reference 11

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