Paper Citation Record · LEDGER

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning

As of 20 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2412.05636.

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

pith.paper-citation-record.v1
2412.05636 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:37:33.233591Z

measured 54 of 54 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T16:47:43.951974Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T18:01:05.926602Z

Reference resolution

53 of 53 outbound references displayed

verified exact0
verified fuzzy41
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bba72eaf-b979-464f-860c-e8a6ffa56964 · outbound

This paper cites Incentive-aware autonomous client participation in federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Incentive-aware autonomous client participation in federated learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:34.131951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 98c77a07-e156-4ffe-a0a1-68a00f50c493 · outbound

This paper cites A novel incentive mechanism for federated learning over wireless communications,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning A novel incentive mechanism for federated learning over wireless communications,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:34.114400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5b66f893-bcf8-4827-92cf-7a33013f3f0e · outbound

This paper cites Validating privacy-preserving face recognition under a minimum assumption,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Validating privacy-preserving face recognition under a minimum assumption,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:34.093757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2e00ec75-eb77-4fa1-a36e-e98cbf7a540b · outbound

This paper cites Disentangle then calibrate with gradient guidance: A unified framework for common and rare disease diagnosis,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Disentangle then calibrate with gradient guidance: A unified framework for common and rare disease diagnosis,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:34.076064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9e67dfa1-0e40-48fc-90c5-0adab631531a · outbound

This paper cites Human-in-the- loop embodied intelligence with interactive simulation environment for surgical robot learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Human-in-the- loop embodied intelligence with interactive simulation environment for surgical robot learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:34.059390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f757ec67-70cf-404a-a2b9-b45cbcd0a7a8 · outbound

This paper cites Adaptive heterogeneous client sampling for federated learning over wireless networks,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Adaptive heterogeneous client sampling for federated learning over wireless networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:34.043112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ce5560f5-e112-4ff8-974b-4b5da2a50a3c · outbound

This paper cites The california consumer privacy act: Towards a european- style privacy regime in the united states,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning The california consumer privacy act: Towards a european- style privacy regime in the united states,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:34.025708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a2262b74-57cf-4b0a-b336-6dfc5f3f15f7 · outbound

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

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.008560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 613be7e6-5e8e-4caf-8f60-838c4922fc1f · outbound

This paper cites Tackling system and statistical heterogeneity for federated learning with adaptive client sampling,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Tackling system and statistical heterogeneity for federated learning with adaptive client sampling,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.997291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 935fb728-bf8b-4b6c-b4c2-6d6b1fdc4b85 · outbound

This paper cites Three-stage stackelberg game enabled clustered federated learning in heterogeneous uav swarms,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Three-stage stackelberg game enabled clustered federated learning in heterogeneous uav swarms,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.980699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a90a0999-1623-4e3b-ac59-03ce6c221225 · outbound

This paper cites Delta: Diverse client sampling for fasting federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Delta: Diverse client sampling for fasting federated learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.964472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 19f323c7-5d35-4866-bc09-2c76355cde90 · outbound

This paper cites Accelerating hybrid feder- ated learning convergence under partial participation,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Accelerating hybrid feder- ated learning convergence under partial participation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.947490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2d3e57fb-3feb-4263-9d3d-17fe49bcb2ac · outbound

This paper cites Incentive-boosted federated crowdsourcing,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Incentive-boosted federated crowdsourcing,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.930319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d991677a-77cd-4deb-859d-a8d0bf79b3f5 · outbound

This paper cites Deep leakage from gradient,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Deep leakage from gradient,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.911439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4879ca8a-1646-4363-bad7-1b960476c659 · outbound

This paper cites Concentrated differential privacy: Simplifications, extensions, and lower bounds,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Concentrated differential privacy: Simplifications, extensions, and lower bounds,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.894741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f7d81f2a-95c4-4e58-aafc-8b7056dad065 · outbound

This paper cites Shield against gradient leakage attacks: Adaptive privacy-preserving federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Shield against gradient leakage attacks: Adaptive privacy-preserving federated learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.878080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cf330914-2c20-4839-8926-e62aaff11082 · outbound

This paper cites Efficient federated learning with enhanced privacy via lottery ticket pruning in edge computing,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Efficient federated learning with enhanced privacy via lottery ticket pruning in edge computing,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.860060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cbc261e3-7334-4740-9c3d-4711920ddb95 · outbound

This paper cites Optimal mechanism design for heterogeneous client sampling in federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Optimal mechanism design for heterogeneous client sampling in federated learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.842633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5252432b-daea-4ba1-bee6-4ad2a67a8b1f · outbound

This paper cites Incentive mechanism for federated learning with random client selection,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Incentive mechanism for federated learning with random client selection,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.064533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d9dbed8-395c-4b7c-ae2f-232d4de508d3 · outbound

This paper cites Dordis: Efficient federated learning with dropout-resilient differential privacy,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Dordis: Efficient federated learning with dropout-resilient differential privacy,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.813426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 15bfb4f8-180d-42c5-bd54-8a8b08277b13 · outbound

This paper cites Personalized local differentially private federated learning with adaptive client sampling,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Personalized local differentially private federated learning with adaptive client sampling,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.796848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9ee67e3d-0978-4b4b-b735-9a71911a81fe · outbound

This paper cites Fed-CBS: A heterogeneity-aware client sampling mechanism for federated learning via class-imbalance reduction,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Fed-CBS: A heterogeneity-aware client sampling mechanism for federated learning via class-imbalance reduction,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.780562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ec616dfb-d3d1-4469-a8e8-9523f9684309 · outbound

This paper cites Anchor sampling for federated learning with partial client participation,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Anchor sampling for federated learning with partial client participation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.763734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1e0584cc-98e0-4072-a63c-15efe229cb8e · outbound

This paper cites Gluefl: Reconciling client sampling and model masking for bandwidth efficient federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Gluefl: Reconciling client sampling and model masking for bandwidth efficient federated learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.746883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1fdef36c-f151-40ba-be3f-59c532f584cf · outbound

This paper cites A personalized privacy preserving mechanism for crowdsourced federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning A personalized privacy preserving mechanism for crowdsourced federated learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.730029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 675d235f-624d-4627-b17c-344f287e4fd8 · outbound

This paper cites Game analysis and incentive mechanism design for differentially private cross-silo federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Game analysis and incentive mechanism design for differentially private cross-silo federated learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.712342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 89e38234-57af-4f1e-ae97-dc1a8f7c89c5 · outbound

This paper cites Collaboration in federated learning with differential privacy: A stackelberg game analysis,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Collaboration in federated learning with differential privacy: A stackelberg game analysis,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.695232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d074c961-a2fe-4f42-86d5-738af36fe98d · outbound

This paper cites Trade privacy for utility: A learning-based privacy pricing game in federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Trade privacy for utility: A learning-based privacy pricing game in federated learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.677881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87e50a74-b054-4195-8d61-3e226c0ee07d · outbound

This paper cites Imfl-aigc: Incentive mechanism design for federated learning empowered by artificial intelligence generated content,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Imfl-aigc: Incentive mechanism design for federated learning empowered by artificial intelligence generated content,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.661479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c318c720-417f-4300-ae44-ee0891e5642f · outbound

This paper cites Trading data for learning: Incentive mechanism for on-device federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Trading data for learning: Incentive mechanism for on-device federated learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.644841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 35228fd8-3776-4897-83a6-59d967f3c424 · outbound

This paper cites The algorithmic foundations of differential privacy,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning The algorithmic foundations of differential privacy,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.123624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42802b90-c06b-41e4-af23-35adea943961 · outbound

This paper cites On the convergence of fedavg on non-iid data,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning On the convergence of fedavg on non-iid data,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.615325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d0c66fe7-be15-47a2-a946-62b2f8e9b1c7 · outbound

This paper cites Fast-convergent federated learning with adaptive weighting,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Fast-convergent federated learning with adaptive weighting,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.597889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e55ad6ed-45f2-48e6-9a3f-cc517057f3f5 · outbound

This paper cites On the stability analysis of open federated learning systems,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning On the stability analysis of open federated learning systems,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.581029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4bac4e33-46d3-41a0-a4fb-6eb3703b9d8a · outbound

This paper cites Incentive mechanism design for federated learning and unlearning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Incentive mechanism design for federated learning and unlearning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.562943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 336bb066-b9de-4750-a22d-7ceeb2514808 · outbound

This paper cites Incentive mechanism for spatial crowdsourcing with unknown social-aware workers: A three-stage stackelberg game approach,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Incentive mechanism for spatial crowdsourcing with unknown social-aware workers: A three-stage stackelberg game approach,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.543561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9ae089a8-1e62-4df2-ac1c-9d2c3d57c38f · outbound

This paper cites Network-constrained stackelberg game for pricing demand flexibility in power distribution systems,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Network-constrained stackelberg game for pricing demand flexibility in power distribution systems,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.525402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 524a8260-aa47-4e91-bee3-9117babe2050 · outbound

This paper cites Non-cooperative game pricing strategy for maximizing social welfare in electrified transportation networks,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Non-cooperative game pricing strategy for maximizing social welfare in electrified transportation networks,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.508196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f4a172ae-3e47-45cf-b48d-af1bb615ccd4 · outbound

This paper cites Algorithms, games, and the internet,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Algorithms, games, and the internet,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.490285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c7f5890b-f2b5-4cfe-b24a-7ba439c58bef · outbound

This paper cites A profit-maximizing model marketplace with differentially private federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning A profit-maximizing model marketplace with differentially private federated learning,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.471390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd57d7a8-c1f5-4dbf-a760-797c7ec76a65 · outbound

This paper cites A socially optimal data marketplace with differentially private federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning A socially optimal data marketplace with differentially private federated learning,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.172514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61a4e7eb-0cbb-486e-93a7-806d3ac98333 · outbound

This paper cites Nonlinear programming,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Nonlinear programming,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.177226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c53f9afb-e1ab-4a3f-ab05-c8c24510fe1b · outbound

This paper cites Optimal client sampling for federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Optimal client sampling for federated learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.431350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ad03bb04-7eae-4e7c-b808-5d92a559afde · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.187242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73ba6659-552b-4853-a4bc-0898d0662029 · outbound

This paper cites Learning multiple layers of features from tiny images,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Learning multiple layers of features from tiny images,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.192508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0bb055e-2b5f-4bdc-afc5-0c0f7ad5d616 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Reading digits in natural images with unsupervised feature learning,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.197066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 250064c7-90fa-4713-a47f-5e82e3b1430a · outbound

This paper cites CINIC-10 is not ImageNet or CIFAR-10.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning CINIC-10 is not ImageNet or CIFAR-10

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.201771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff0a4ed6-30f8-4cb7-a62d-f60f5244f531 · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.207101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e895008-3b0c-4542-b596-352fb208af06 · outbound

This paper cites Feddisco: Fed- erated learning with discrepancy-aware collaboration,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Feddisco: Fed- erated learning with discrepancy-aware collaboration,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.212662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20b70e56-410e-42b0-aeec-2f0b33337c35 · outbound

This paper cites Towards instance-adaptive inference for federated learning,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Towards instance-adaptive inference for federated learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.378821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 588cdf1f-1bd1-4a6a-9290-78d3fd999c7e · outbound

This paper cites Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:33.222506Z

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Unavailable: canonical work link unavailable.

Observation 1030ffe4-3d8c-4e7a-a8b5-d3a30bfd6936 · outbound

This paper cites Discrete lq optimal control with integral action: A simple controller on incremental form for mimo systems,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Discrete lq optimal control with integral action: A simple controller on incremental form for mimo systems,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.362472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 413c8be2-d13d-4408-b105-b7c43a3a2922 · outbound

This paper cites Linear–quadratic optimal control for discrete-time mean-field systems with input delay,.

A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning Linear–quadratic optimal control for discrete-time mean-field systems with input delay,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:33.345445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 412aab5b-f31c-4558-90a7-52a2279e1a9b · inbound

MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria cites this paper.

MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:01:05.928692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.