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Paper Citation Record · LEDGER

An Adaptive Differentially Private Federated Learning Framework

As of 16 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2602.06838.

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

pith.paper-citation-record.v1
2602.06838 v3

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measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:50:09.401292Z

measured 32 of 32 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

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Reference resolution

32 of 32 outbound references displayed

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Outbound references

Observation 7387d9eb-638b-48bc-b68e-77cdb90f6ed1 · outbound

This paper cites Cellular traffic prediction via byzantine- robust asynchronous federated learning,.

An Adaptive Differentially Private Federated Learning Framework Cellular traffic prediction via byzantine- robust asynchronous federated learning,

Reference 1

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Observation 790243bb-bf65-484d-a681-a2c66e1de137 · outbound

This paper cites Image classification using federated learning,.

An Adaptive Differentially Private Federated Learning Framework Image classification using federated learning,

Reference 2

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Observation 95da676e-173d-4d3a-a6f5-bbd8c5b3b8b0 · outbound

This paper cites Enhancing medical image classification via federated learning and pre-trained model,.

An Adaptive Differentially Private Federated Learning Framework Enhancing medical image classification via federated learning and pre-trained model,

Reference 3

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Observation ca661e90-5447-477a-ae42-d19c15756d73 · outbound

This paper cites FGS-FL: Enhancing federated learning with differential privacy via flat gradient stream,.

An Adaptive Differentially Private Federated Learning Framework FGS-FL: Enhancing federated learning with differential privacy via flat gradient stream,

Reference 4

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Observation d47e7db8-fdf4-4db8-933a-f7b5bc6078e7 · outbound

This paper cites Federated semi-supervised medical image classification via inter-client relation matching,.

An Adaptive Differentially Private Federated Learning Framework Federated semi-supervised medical image classification via inter-client relation matching,

Reference 5

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Observation 15592a0f-9b23-4b2e-b33c-cdbe7c6c6ede · outbound

This paper cites Uldp-FL: Federated learning with across-silo user-level differential privacy,.

An Adaptive Differentially Private Federated Learning Framework Uldp-FL: Federated learning with across-silo user-level differential privacy,

Reference 6

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Observation 8f20d7a9-4316-44b4-9575-dafda53fe1bd · outbound

This paper cites Dyn-D2P: Dynamic decentralized differential privacy for peer-to-peer federated learning,.

An Adaptive Differentially Private Federated Learning Framework Dyn-D2P: Dynamic decentralized differential privacy for peer-to-peer federated learning,

Reference 7

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Observation b8653961-69e9-4d12-84d8-58809f363d1c · outbound

This paper cites Toward the flatter landscape and better generalization in federated learning under client-level differ- ential privacy,.

An Adaptive Differentially Private Federated Learning Framework Toward the flatter landscape and better generalization in federated learning under client-level differ- ential privacy,

Reference 8

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source=pdf_text observed=2026-08-03T03:50:07.154735Z digest=sha256:ecc1fffd14cf37a9e726016c512ab9e0dcd0e9efba9a725aa15fdca1a1ad9059

Observation f521b761-95de-457d-8ec7-0bb122919b85 · outbound

This paper cites One-shot empirical privacy estimation for federated learning,.

An Adaptive Differentially Private Federated Learning Framework One-shot empirical privacy estimation for federated learning,

Reference 9

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Observation 32556e75-868c-4b42-a4a3-187d26e88f34 · outbound

This paper cites Asynchronous decentralized federated anomaly detection for 6G networks,.

An Adaptive Differentially Private Federated Learning Framework Asynchronous decentralized federated anomaly detection for 6G networks,

Reference 10

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Observation 1ed85561-e5f1-4a13-a242-023909b15fa5 · outbound

This paper cites Belt and braces: When federated learning meets differential privacy,.

An Adaptive Differentially Private Federated Learning Framework Belt and braces: When federated learning meets differential privacy,

Reference 11

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Observation 52ed07b8-4cf7-47b3-ad69-1311d76ff0b4 · outbound

This paper cites Siren ++: Robust federated learning with proactive alarming and differential privacy,.

An Adaptive Differentially Private Federated Learning Framework Siren ++: Robust federated learning with proactive alarming and differential privacy,

Reference 12

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Observation 82393375-ff9d-40a4-aa0d-1141467babe3 · outbound

This paper cites CAMEL: Communication-efficient and maliciously secure federated learning in the shuffle model of differential privacy,.

An Adaptive Differentially Private Federated Learning Framework CAMEL: Communication-efficient and maliciously secure federated learning in the shuffle model of differential privacy,

Reference 13

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Observation 7e247b6e-5190-49a1-b979-691a20c33dc7 · outbound

This paper cites Noise-aware algorithm for heterogeneous differentially private federated learning,.

An Adaptive Differentially Private Federated Learning Framework Noise-aware algorithm for heterogeneous differentially private federated learning,

Reference 14

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Observation 39ad4457-193f-45de-9572-1a476c05829c · outbound

This paper cites Federated learning with sparsified model perturbation: Improving accuracy under client-level differential privacy,.

An Adaptive Differentially Private Federated Learning Framework Federated learning with sparsified model perturbation: Improving accuracy under client-level differential privacy,

Reference 15

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Observation 81cf1190-e101-4b55-aaf7-8b137fec1718 · outbound

This paper cites Harnessing sparsification in federated learning: A secure, efficient, and differentially private realization,.

An Adaptive Differentially Private Federated Learning Framework Harnessing sparsification in federated learning: A secure, efficient, and differentially private realization,

Reference 16

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source=pdf_text observed=2026-08-03T03:50:07.912145Z digest=sha256:718b81e56856b84dda6156184f6db65105aa00ad89c6341f4eb1ce4deb9c66b9

Observation e02d0716-0720-4028-8b0a-0bdea981037d · outbound

This paper cites Efficient federated learning privacy preservation method with heterogeneous differential privacy,.

An Adaptive Differentially Private Federated Learning Framework Efficient federated learning privacy preservation method with heterogeneous differential privacy,

Reference 17

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source=pdf_text observed=2026-08-03T03:50:07.986974Z digest=sha256:ea4f03e3df276bad20b5aa1f133a47aa34b44efdf5a54698f9cd5bfbb5fccf0b

Observation 429dbc70-a947-47a3-9007-60ce8889df0d · outbound

This paper cites Differentially private fed- erated learning with time-adaptive privacy spending,.

An Adaptive Differentially Private Federated Learning Framework Differentially private fed- erated learning with time-adaptive privacy spending,

Reference 18

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Observation 33bd615c-d803-4dc3-ada2-d14de84fd3d1 · outbound

This paper cites Privacy-preserving federated learning for industrial edge computing via hybrid differential privacy and adaptive compression,.

An Adaptive Differentially Private Federated Learning Framework Privacy-preserving federated learning for industrial edge computing via hybrid differential privacy and adaptive compression,

Reference 19

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Observation cd933790-498d-417d-8076-f1b6e10859c1 · outbound

This paper cites Local differential privacy feder- ated learning based on heterogeneous data multi-privacy mechanism,.

An Adaptive Differentially Private Federated Learning Framework Local differential privacy feder- ated learning based on heterogeneous data multi-privacy mechanism,

Reference 20

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Observation 49ad84a0-654f-4003-ab12-697623c9f958 · outbound

This paper cites DP-PSAC-FL: Differentially private federated learning based on per-sample adaptive clipping and layer- wise gradient perturbation,.

An Adaptive Differentially Private Federated Learning Framework DP-PSAC-FL: Differentially private federated learning based on per-sample adaptive clipping and layer- wise gradient perturbation,

Reference 21

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Observation 950b792f-b382-40ef-af3d-6ca48b9f4de2 · outbound

This paper cites Towards the robustness of dif- ferentially private federated learning,.

An Adaptive Differentially Private Federated Learning Framework Towards the robustness of dif- ferentially private federated learning,

Reference 22

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Observation c787abaa-ffc4-4099-9428-b7e66d19b242 · outbound

This paper cites Dynamic sparse training with structured sparsity,.

An Adaptive Differentially Private Federated Learning Framework Dynamic sparse training with structured sparsity,

Reference 23

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Observation ce16e84c-79c6-4adf-8369-73e3f28ccc49 · outbound

This paper cites An improved analysis of per- sample and per-update clipping in federated learning,.

An Adaptive Differentially Private Federated Learning Framework An improved analysis of per- sample and per-update clipping in federated learning,

Reference 24

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Observation 0f905731-2457-4b41-b7fb-4cf85db38be1 · outbound

This paper cites Adaptive clipping for differential private federated learning in interpolation regimes,.

An Adaptive Differentially Private Federated Learning Framework Adaptive clipping for differential private federated learning in interpolation regimes,

Reference 25

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Observation 213bcdfe-a834-4934-91c3-af1ec409b0ae · outbound

This paper cites Exponential moving average of weights in deep learning: Dynamics and benefits,.

An Adaptive Differentially Private Federated Learning Framework Exponential moving average of weights in deep learning: Dynamics and benefits,

Reference 26

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Observation 59e71677-0701-491c-9ace-7b888c05318c · outbound

This paper cites Distributed distributionally robust optimization with non-convex objectives,.

An Adaptive Differentially Private Federated Learning Framework Distributed distributionally robust optimization with non-convex objectives,

Reference 27

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source=pdf_text observed=2026-08-03T03:50:08.940439Z digest=sha256:112d4b4bc4b705e39788fb0386e7e24ffa56107a6d56a076111cc0bc35e0c141

Observation c4d85b92-8a28-45cb-96e6-ecb21a19dee5 · outbound

This paper cites Understanding global ag- gregation and optimization of federated learning,.

An Adaptive Differentially Private Federated Learning Framework Understanding global ag- gregation and optimization of federated learning,

Reference 28

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Observation 0527c60d-6b99-4ff7-b1ed-c1ebf98d8fe6 · outbound

This paper cites Self-adaptive asynchronous federated optimizer with adversarial sharpness-aware minimization,.

An Adaptive Differentially Private Federated Learning Framework Self-adaptive asynchronous federated optimizer with adversarial sharpness-aware minimization,

Reference 29

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source=pdf_text observed=2026-08-03T03:50:09.118906Z digest=sha256:86128c334922787bcffdf61ff2e14b81c1d1fb097578a3431531158cd4f683b8

Observation a9452bbc-3139-4fb7-8b94-7a797e8f52f7 · outbound

This paper cites Differentially private federated learning with an adaptive noise mechanism,.

An Adaptive Differentially Private Federated Learning Framework Differentially private federated learning with an adaptive noise mechanism,

Reference 30

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Observation 0286cad9-a0e7-4b14-bff8-7679db4d75de · outbound

This paper cites Differentially private federated learning on Non-IID data: Convergence analysis and adaptive opti- mization,.

An Adaptive Differentially Private Federated Learning Framework Differentially private federated learning on Non-IID data: Convergence analysis and adaptive opti- mization,

Reference 31

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Observation b353b11e-0355-488e-9b87-3fd871287a03 · outbound

This paper cites A lightweight differentially private federated learning framework for edge intelligence,.

An Adaptive Differentially Private Federated Learning Framework A lightweight differentially private federated learning framework for edge intelligence,

Reference 32

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Pith citing papers

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