Paper Citation Record · LEDGER

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey

As of 22 August 2026, this Paper Citation Record lists 100 of 280 outbound references and 3 inbound Pith citation observations for arXiv:2412.13840.

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

pith.paper-citation-record.v1
2412.13840 v2

Coverage vector

measured 100 of 280 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:46:50.291274Z

measured 103 of 103 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:07:33.185236Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T22:47:39.315457Z

Reference resolution

100 of 280 outbound references displayed

verified exact3
verified fuzzy0
unresolved96
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f0df552a-6e30-4c79-b534-f441b3ffc373 · outbound

This paper cites Deep learning,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Deep learning,

Reference 1

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Observation 9466097b-a3fd-48dd-9b66-053719071521 · outbound

This paper cites A unified theory of decentralized sgd with changing topology and local updates,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A unified theory of decentralized sgd with changing topology and local updates,

Reference 2

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Observation 844deace-611b-408c-8de8-fcb81b81d2bf · outbound

This paper cites Decentralized vs. distributed organization: Blockchain, machine learning and the future of the digital platform,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Decentralized vs. distributed organization: Blockchain, machine learning and the future of the digital platform,

Reference 3

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Observation 02ca746b-7c2b-473b-83ad-52deacb33f70 · outbound

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

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Communication-efficient learning of deep networks from decentral- ized data,

Reference 4

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Observation 5fc0cc28-3096-49ed-85d5-492f8519e601 · outbound

This paper cites Reliable federated learning for mobile networks,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Reliable federated learning for mobile networks,

Reference 5

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Observation a331ad4e-2d01-4878-9ff7-55a5191ff544 · outbound

This paper cites Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services,

Reference 6

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Observation ef091cf3-7455-474a-a386-816c154c54b4 · outbound

This paper cites Cloud/edge computing service management in blockchain networks: Multi-leader multi-follower game-based admm for pricing,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Cloud/edge computing service management in blockchain networks: Multi-leader multi-follower game-based admm for pricing,

Reference 7

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Observation e7c12845-d97b-4896-aad1-66bda6869d70 · outbound

This paper cites The non-iid data quagmire of decentralized machine learning,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey The non-iid data quagmire of decentralized machine learning,

Reference 8

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Observation 7fe7200b-e0b3-4886-ac86-701d5846970f · outbound

This paper cites A consensus-based decentralized training al- gorithm for deep neural networks with communication compression,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A consensus-based decentralized training al- gorithm for deep neural networks with communication compression,

Reference 9

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Observation 6f5c8e39-3154-4f7a-8d9b-edd15ba83db4 · outbound

This paper cites Byzantine-robust decentralized federated learning via dual-domain clustering and trust bootstrapping,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Byzantine-robust decentralized federated learning via dual-domain clustering and trust bootstrapping,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T12:46:49.801866Z

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Observation 4803a81a-1df9-419c-9c1a-24e4db91964e · outbound

This paper cites Advances and open problems in federated learning,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Advances and open problems in federated learning,

Reference 11

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Observation 2d418a04-5824-4700-94ee-9eb211d61426 · outbound

This paper cites Dafkd: Domain-aware federated knowledge distillation,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Dafkd: Domain-aware federated knowledge distillation,

Reference 12

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Observation 135678c3-fa7c-4bbe-bfd5-38cdc88caadc · outbound

This paper cites Losp: Overlap synchronization parallel with local compensation for fast dis- tributed training,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Losp: Overlap synchronization parallel with local compensation for fast dis- tributed training,

Reference 13

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Observation 1c6bb5ee-5b5b-4c91-b3e3-8aa92e7913f8 · outbound

This paper cites Edge computing: Vision and challenges,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Edge computing: Vision and challenges,

Reference 14

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Observation 34261c8e-5c50-40d5-a4ff-61f2717e17dc · outbound

This paper cites A survey on mobile edge computing: The communication perspective,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A survey on mobile edge computing: The communication perspective,

Reference 15

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Observation d1a63af0-06fa-4b6c-8b82-fdbb12ec8a9f · outbound

This paper cites Flee: A hierarchical federated learning framework for distributed deep neural network over cloud, edge, and end device,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Flee: A hierarchical federated learning framework for distributed deep neural network over cloud, edge, and end device,

Reference 16

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Observation 32beed6b-ca8d-4755-9603-c7673b61ad8d · outbound

This paper cites Personalized federated recommendation for cold-start users via adaptive knowledge fusion,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Personalized federated recommendation for cold-start users via adaptive knowledge fusion,

Reference 17

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Observation 3bfe8204-e397-4379-ae51-544c338a8755 · outbound

This paper cites A systematic survey on federated sequential recommendation,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A systematic survey on federated sequential recommendation,

Reference 18

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Observation 7b81178a-d889-4b07-8aea-b32fcd30143e · outbound

This paper cites Decentralized graph neural network for privacy-preserving recommendation,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Decentralized graph neural network for privacy-preserving recommendation,

Reference 19

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Observation 2958e75a-2a89-452b-8459-64b0c610e7aa · outbound

This paper cites En- abling localized peer-to-peer electricity trading among plug-in hybrid electric vehicles using consortium blockchains,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey En- abling localized peer-to-peer electricity trading among plug-in hybrid electric vehicles using consortium blockchains,

Reference 20

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Observation dc9fffe5-de6e-4362-9a27-dd841277c8e8 · outbound

This paper cites Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory,

Reference 21

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Observation c1af3fac-a111-4f40-a127-0742e4a3333f · outbound

This paper cites Generative ai-empowered simulation for autonomous driving in vehicular mixed reality metaverses,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Generative ai-empowered simulation for autonomous driving in vehicular mixed reality metaverses,

Reference 22

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Observation efe43600-776a-4679-a519-4997463a501f · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Overcoming catastrophic forgetting in neural networks,

Reference 23

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Observation 6d466276-f47a-4d29-9656-2e1e9ed8e21c · outbound

This paper cites Catastrophic forgetting, rehearsal and pseudorehearsal,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Catastrophic forgetting, rehearsal and pseudorehearsal,

Reference 24

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Observation 098c601a-45f7-430c-bca4-e45305861904 · outbound

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Catastrophic forgetting in connectionist networks,

Reference 25

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Observation 0141a32d-ce1e-4f73-b153-aef10e1c204c · outbound

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

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Heterogeneous federated learning: State-of-the-art and research challenges,

Reference 26

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Observation 53542cf3-c2c6-486c-a478-1b6134ce68bb · outbound

This paper cites Heterogeneous edge computing open platforms and tools for internet of things,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Heterogeneous edge computing open platforms and tools for internet of things,

Reference 27

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Observation 318ef999-d664-45e1-bd7e-818ac9dd043b · outbound

This paper cites A survey on federated learning,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A survey on federated learning,

Reference 28

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Observation d948fef1-4730-4304-b166-bbb31afdcf09 · outbound

This paper cites Federated learning in mobile edge networks: A comprehensive survey,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Federated learning in mobile edge networks: A comprehensive survey,

Reference 29

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Observation 0f6983f3-e57c-41b7-8df3-4fe7b17b4130 · outbound

This paper cites Federated learning for internet of things: Recent advances, taxonomy, and open challenges,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Federated learning for internet of things: Recent advances, taxonomy, and open challenges,

Reference 30

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Observation 94a1ca38-b72d-4c56-b039-345798381169 · outbound

This paper cites A survey on federated learning for resource-constrained iot devices,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A survey on federated learning for resource-constrained iot devices,

Reference 31

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This paper cites Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges,

Reference 32

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

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A continual learning survey: Defying forgetting in classification tasks,

Reference 33

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This paper cites Three types of incremental learning,.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Three types of incremental learning,

Reference 34

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Class-incremental learning: survey and performance evaluation on image classification,

Reference 35

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Observation 68b14e3c-5b34-44fc-9b14-1c4be645a86a · outbound

This paper cites A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration.

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration

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

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Non-iid data and continual learning processes in federated learning: A long road ahead,

Reference 37

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Federated continual learning via knowledge fusion: A survey,

Reference 38

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Traceable federated continual learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Federated continual learning with weighted inter-client transfer,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Continual federated learning based on knowledge distillation

Reference 41

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Better generative replay for continual federated learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Target: Federated class- continual learning via exemplar-free distillation,

Reference 43

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Accurate forgetting for heterogeneous federated continual learning,

Reference 44

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Sr-fdil: Synergistic replay for federated domain-incremental learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Towards Efficient Replay in Federated Incremental Learning

Reference 46

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Re-fed+: A better replay strategy for federated incremental learning,

Reference 47

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Federated incremental semantic segmentation,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey No one left behind: Real-world federated class-incremental learning,

Reference 49

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Urban traffic forecast- ing using federated and continual learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A survey on distributed machine learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A survey of mobile cloud computing: architecture, applications, and approaches,

Reference 53

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Feddse: Distribution-aware sub-model extraction for feder- ated learning over resource-constrained devices,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Data poisoning attacks on federated machine learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Decentralized federated learning: Balancing communication and computing costs,

Reference 56

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Decentralized learning for wireless communications and networking,

Reference 57

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey The non-iid data quagmire of decentralized machine learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Learning to collaborate in decentralized learning of personalized models,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Gadmm: Fast and communication efficient framework for distributed machine learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Advances and open problems in federated learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Fednlr: Federated learning with neuron-wise learning rates,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Fedcda: Federated learning with cross-rounds divergence-aware aggregation,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Communication-efficient learning of deep networks from decentral- ized data,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Federated optimization in heterogeneous networks,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Model-contrastive federated learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Scaffold: Stochastic controlled averaging for federated learn- ing,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Group-based hierarchical federated learning: Convergence, group formation, and sampling,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Dynamic edge association and resource allocation in self-organizing hierarchical federated learning networks,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Hfel: Joint edge association and resource allocation for cost-efficient hierarchical feder- ated edge learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Enhanced hybrid hierarchical federated edge learning over heterogeneous networks,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Towards effective clustered federated learning: A peer-to-peer framework with adaptive neighbor matching,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A novel federated learning approach based on the confidence of federated kalman filters,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Federated meta-learning for spatial-temporal prediction,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Peer-to-peer variational federated learning over arbitrary graphs,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Non-interactive verifiable privacy-preserving federated learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey [retracted] aflpc: An asynchronous federated learning privacy-preserving comput- ing model applied to 5g-v2x,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Privacy-preserving federated learning using homomorphic encryption,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Trusted decentralized federated learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey The algorithmic foundations of differential privacy,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Federated learning for resource-constrained iot devices: Panoramas and state of the art,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Fairness-aware federated learning with unreliable links in resource-constrained internet of things,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Chen and B

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Continual lifelong learning with neural networks: A review,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Expert gate: Lifelong learning with a network of experts,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey A bio-inspired incremental learning architecture for applied perceptual problems,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Incremental learning through deep adaptation,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey icarl: Incremental classifier and representation learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Gradient episodic memory for con- tinual learning,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Overcoming catastrophic forgetting in neural networks,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Continual learning through synaptic intelligence,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Learning without forgetting,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Overcoming catastrophic forgetting with hard attention to the task,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Overcoming catastrophic forgetting during domain adaptation of seq2seq language generation,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Prompt augmented generative replay via supervised contrastive training for lifelong intent detection,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Fine-tuned Language Models are Continual Learners

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Computationally budgeted con- tinual learning: What does matter?

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Generative replay with feedback connections as a general strategy for continual learning

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Pa- rameterizing federated continual learning for reproducible research,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Personalized federated class- incremental learning through critical parameter transfer,

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Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching

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FedGIG: Graph Inversion from Gradient in Federated Learning Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey

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Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey

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Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learning Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey

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