Incentivizing High-quality Participation From Federated Learning Agents | Paper Citation Record

Observation a1d98bc5-9d66-4e50-82dd-b87bf0be7c47 · outbound

This paper cites Programming pearls: algorithm design techniques.

Incentivizing High-quality Participation From Federated Learning Agents Programming pearls: algorithm design techniques

Reference 1

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Observation 76f1fae5-afc4-49b1-94c6-da47e0f71656 · outbound

This paper cites One for one, or all for all: Equilibria and optimality of collaboration in federated learning.

Incentivizing High-quality Participation From Federated Learning Agents One for one, or all for all: Equilibria and optimality of collaboration in federated learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.786494Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ff7159c5-df99-45e8-a270-8fb4096e2579 · outbound

This paper cites \"U ber abbildung von mannigfaltigkeiten.

Incentivizing High-quality Participation From Federated Learning Agents \"U ber abbildung von mannigfaltigkeiten

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.766773Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 503d8ce3-7347-4974-8469-f0b5d12d55fa · outbound

This paper cites Maximizing Global Model Appeal in Federated Learning.

Incentivizing High-quality Participation From Federated Learning Agents Maximizing Global Model Appeal in Federated Learning

Reference 4

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Observation a4dbc53e-d91b-438e-b1ae-8c34ea61213a · outbound

This paper cites Maximizing global model appeal in federated learning, 2023.

Incentivizing High-quality Participation From Federated Learning Agents Maximizing global model appeal in federated learning, 2023

Reference 5

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6845a978-5727-4a37-bc34-6f532f192976 · outbound

This paper cites On the Convergence of Federated Averaging with Cyclic Client Participation.

Incentivizing High-quality Participation From Federated Learning Agents On the Convergence of Federated Averaging with Cyclic Client Participation

Reference 6

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ed19546-1bf4-4300-9fbb-e3e24cf84f29 · outbound

This paper cites Collaboration equilibrium in federated learning.

Incentivizing High-quality Participation From Federated Learning Agents Collaboration equilibrium in federated learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.730700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c04c11d3-8f00-44b9-8f29-dcd793ac4590 · outbound

This paper cites Tackling data heterogeneity in federated learning with class prototypes.

Incentivizing High-quality Participation From Federated Learning Agents Tackling data heterogeneity in federated learning with class prototypes

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.713450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 98e9e266-f84e-4cbc-91c9-b2ca3a6340de · outbound

This paper cites Model-sharing games: Analyzing federated learning under voluntary participation.

Incentivizing High-quality Participation From Federated Learning Agents Model-sharing games: Analyzing federated learning under voluntary participation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.695752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0a149cfd-2c3e-41c8-bb48-ff93b9c99e42 · outbound

This paper cites Optimality and stability in federated learning: A game-theoretic approach.

Incentivizing High-quality Participation From Federated Learning Agents Optimality and stability in federated learning: A game-theoretic approach

Reference 10

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Source-reported events for the cited work

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Observation 16ef7d7b-f2db-4c0c-8060-c7c03b510b0a · outbound

This paper cites Online scheduling unbiased distributed learning over wireless edge networks.

Incentivizing High-quality Participation From Federated Learning Agents Online scheduling unbiased distributed learning over wireless edge networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.669102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation daf13277-82ff-4b5a-8f9f-39c81695200b · outbound

This paper cites Tokenized incentive for federated learning.

Incentivizing High-quality Participation From Federated Learning Agents Tokenized incentive for federated learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.651445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ff52e95f-0aac-420c-ac15-036ace155308 · outbound

This paper cites Incentive Mechanism Design for Federated Learning: Hedonic Game Approach.

Incentivizing High-quality Participation From Federated Learning Agents Incentive Mechanism Design for Federated Learning: Hedonic Game Approach

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.492351Z

Source-reported events for the cited work

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Observation 48cf966f-10d7-42bb-90ec-a65b2ea4148e · outbound

This paper cites Towards practical overlay networks for decentralized federated learning.

Incentivizing High-quality Participation From Federated Learning Agents Towards practical overlay networks for decentralized federated learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.633787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 47dbc860-56e7-4bf2-a7ef-96edf54b4475 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Incentivizing High-quality Participation From Federated Learning Agents Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 15

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Source-reported events for the cited work

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Observation 9208fe02-fbfa-46bf-ac04-2ee81b15c07a · outbound

This paper cites Incentive design for efficient federated learning in mobile networks: A contract theory approach.

Incentivizing High-quality Participation From Federated Learning Agents Incentive design for efficient federated learning in mobile networks: A contract theory approach

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.606469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8896c206-b8ff-4e4f-a664-9e53bd45a0da · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learning.

Incentivizing High-quality Participation From Federated Learning Agents Scaffold: Stochastic controlled averaging for federated learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.590083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 080248e1-111c-47b0-a055-00f648b22794 · outbound

This paper cites Mechanisms that Incentivize Data Sharing in Federated Learning.

Incentivizing High-quality Participation From Federated Learning Agents Mechanisms that Incentivize Data Sharing in Federated Learning

Reference 18

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Source-reported events for the cited work

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Observation 0f9ff0de-1d0b-4ae9-9c7c-0e4b0203bfab · outbound

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

Incentivizing High-quality Participation From Federated Learning Agents Federated learning for edge networks: Resource optimization and incentive mechanism

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.521196Z

Source-reported events for the cited work

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Observation 1676ee89-efc8-4bf4-ab84-3b96da062dba · outbound

This paper cites Adam: A method for stochastic optimization.

Incentivizing High-quality Participation From Federated Learning Agents Adam: A method for stochastic optimization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.561756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6a6f682a-a50f-482a-b210-a7138195b267 · outbound

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

Incentivizing High-quality Participation From Federated Learning Agents A unified theory of decentralized sgd with changing topology and local updates

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.543932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d2053ee0-8288-4a2e-9e5a-1b7b332b9282 · outbound

This paper cites Incentivizing federated learning, 2022.

Incentivizing High-quality Participation From Federated Learning Agents Incentivizing federated learning, 2022

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.525757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12410ba5-bd0d-40ac-a820-8cfcd438950c · outbound

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

Incentivizing High-quality Participation From Federated Learning Agents Learning multiple layers of features from tiny images

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.539559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9afb5e37-ced0-4226-81aa-4dfc0b9aff87 · outbound

This paper cites Gradient-based learning applied to document recognition.

Incentivizing High-quality Participation From Federated Learning Agents Gradient-based learning applied to document recognition

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.544350Z

Source-reported events for the cited work

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Observation 40c38988-f680-4aa2-ba04-9ceb5aee0fe9 · outbound

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

Incentivizing High-quality Participation From Federated Learning Agents On the convergence of fedavg on non-iid data

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.488388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 68fac83a-f3ae-45e1-b22f-1ea40da7f459 · outbound

This paper cites Federated optimization in heterogeneous networks.

Incentivizing High-quality Participation From Federated Learning Agents Federated optimization in heterogeneous networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.471041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3973782b-a2c9-4324-9ae1-2d4f8490c9c2 · outbound

This paper cites Incentives for Federated Learning: a Hypothesis Elicitation Approach.

Incentivizing High-quality Participation From Federated Learning Agents Incentives for Federated Learning: a Hypothesis Elicitation Approach

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.559018Z

Source-reported events for the cited work

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Observation f8f3b7ff-3ae1-4a9a-9f13-7c9a290bb7b3 · outbound

This paper cites Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond.

Incentivizing High-quality Participation From Federated Learning Agents Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.564567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 245b1418-8ed9-45e2-a969-e1e23cf0f6be · outbound

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

Incentivizing High-quality Participation From Federated Learning Agents Communication-efficient learning of deep networks from decentralized data

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.569447Z

Source-reported events for the cited work

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Observation 545c8935-081e-4266-90ea-6691946ba0c4 · outbound

This paper cites Eliciting informative feedback: The peer-prediction method.

Incentivizing High-quality Participation From Federated Learning Agents Eliciting informative feedback: The peer-prediction method

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.441896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 30cbc513-2f12-46eb-875a-b65c3560ca86 · outbound

This paper cites Online scheduling algorithms for unbiased distributed learning over wireless edge networks.

Incentivizing High-quality Participation From Federated Learning Agents Online scheduling algorithms for unbiased distributed learning over wireless edge networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.423880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7a48b095-191c-4bf1-9cfb-243fde2d09c7 · outbound

This paper cites An incentive auction for heterogeneous client selection in federated learning.

Incentivizing High-quality Participation From Federated Learning Agents An incentive auction for heterogeneous client selection in federated learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.407308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc4db552-cf3d-42bb-8b23-f023e91adb3b · outbound

This paper cites Eris: An online auction for scheduling unbiased distributed learning over edge networks.

Incentivizing High-quality Participation From Federated Learning Agents Eris: An online auction for scheduling unbiased distributed learning over edge networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.390897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 14f7e75c-304f-4a19-a397-3b457498ee4a · outbound

This paper cites Fairness without harm: An influence-guided active sampling approach.

Incentivizing High-quality Participation From Federated Learning Agents Fairness without harm: An influence-guided active sampling approach

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.374611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 284356a5-4471-47d1-8ed9-0680e3d6b41e · outbound

This paper cites Improving Data Efficiency via Curating LLM-Driven Rating Systems.

Incentivizing High-quality Participation From Federated Learning Agents Improving Data Efficiency via Curating LLM-Driven Rating Systems

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.598499Z

Source-reported events for the cited work

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Observation ff2c4e9f-6e09-483a-82ce-7e7027d15976 · outbound

This paper cites Token cleaning: Fine-grained data selection for llm supervised fine-tuning.

Incentivizing High-quality Participation From Federated Learning Agents Token cleaning: Fine-grained data selection for llm supervised fine-tuning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.603724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fb35b61-bbde-4c5f-87e0-509d8d6ab5d2 · outbound

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

Incentivizing High-quality Participation From Federated Learning Agents Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.608535Z

Source-reported events for the cited work

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Observation b1e3e0be-f9bc-4709-8eb7-c185ae1b6fcb · outbound

This paper cites Informed truthfulness in multi-task peer prediction.

Incentivizing High-quality Participation From Federated Learning Agents Informed truthfulness in multi-task peer prediction

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.358429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 44255072-9503-43ee-86c6-83283ef7f9e0 · outbound

This paper cites Local SGD Converges Fast and Communicates Little.

Incentivizing High-quality Participation From Federated Learning Agents Local SGD Converges Fast and Communicates Little

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.617905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1694b457-6e3c-43ce-9240-3df4c645426f · outbound

This paper cites Scipy 1.0: fundamental algorithms for scientific computing in python.

Incentivizing High-quality Participation From Federated Learning Agents Scipy 1.0: fundamental algorithms for scientific computing in python

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.623055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 453651d2-a3c2-4f32-a573-569c6a7d935e · outbound

This paper cites Adaptive federated learning in resource constrained edge computing systems.

Incentivizing High-quality Participation From Federated Learning Agents Adaptive federated learning in resource constrained edge computing systems

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.329276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d67dd4ed-e80f-4a4f-b14a-3d935980e3e4 · outbound

This paper cites A principled approach to data valuation for federated learning.

Incentivizing High-quality Participation From Federated Learning Agents A principled approach to data valuation for federated learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.312486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9b1c3b81-386e-4d10-b407-504cb4b491ed · outbound

This paper cites Sample elicitation.

Incentivizing High-quality Participation From Federated Learning Agents Sample elicitation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.295408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8bdf7076-53f9-4a4c-9e5c-8873ba581833 · outbound

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

Incentivizing High-quality Participation From Federated Learning Agents Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.641756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a266a77c-e26b-4637-b5ce-4e83c9bf1d55 · outbound

This paper cites Gradient driven rewards to guarantee fairness in collaborative machine learning.

Incentivizing High-quality Participation From Federated Learning Agents Gradient driven rewards to guarantee fairness in collaborative machine learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.278489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 50623b3b-5edc-4a24-b8aa-fbe3206df2ac · outbound

This paper cites Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning.

Incentivizing High-quality Participation From Federated Learning Agents Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.651043Z

Source-reported events for the cited work

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Observation 1b37d08f-7dd7-43ac-882f-7fcd2e3a9ee3 · outbound

This paper cites Anarchic federated learning.

Incentivizing High-quality Participation From Federated Learning Agents Anarchic federated learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.259828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5fa08999-7ebf-4990-b3e1-edcccc34113e · outbound

This paper cites Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning.

Incentivizing High-quality Participation From Federated Learning Agents Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.241976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9c38bde2-f8c3-440b-8adc-6c27652b26cc · outbound

This paper cites A fairness-aware incentive scheme for federated learning.

Incentivizing High-quality Participation From Federated Learning Agents A fairness-aware incentive scheme for federated learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.224356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0f4df538-7fea-4dbb-9089-5c5d0b568622 · outbound

This paper cites Faithful edge federated learning: Scalability and privacy.

Incentivizing High-quality Participation From Federated Learning Agents Faithful edge federated learning: Scalability and privacy

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.207843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6b8effcd-3355-4b10-954f-9984a81b68bf · outbound

This paper cites Evaluating llm-corrupted crowdsourcing data without ground truth.

Incentivizing High-quality Participation From Federated Learning Agents Evaluating llm-corrupted crowdsourcing data without ground truth

Reference 51

Resolution: verified exact; raw_fallback, observed 2026-08-15T19:26:45.836046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cd1861c6-9a4e-465b-a9e7-f9786d2c6d5f · outbound

This paper cites Federated Learning with Non-IID Data.

Incentivizing High-quality Participation From Federated Learning Agents Federated Learning with Non-IID Data

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.678894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 917241d5-2229-40b4-8522-e5a7f7a78fe7 · outbound

This paper cites A truthful procurement auction for incentivizing heterogeneous clients in federated learning.

Incentivizing High-quality Participation From Federated Learning Agents A truthful procurement auction for incentivizing heterogeneous clients in federated learning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.191041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 75ca8313-19dd-4dff-b1a8-6422e69032f2 · outbound

This paper cites Online scheduling algorithm for heterogeneous distributed machine learning jobs.

Incentivizing High-quality Participation From Federated Learning Agents Online scheduling algorithm for heterogeneous distributed machine learning jobs

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:26:46.174647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6ba432b2-7c60-4373-93ab-2ed4dca55c1a · outbound

This paper cites write newline.

Incentivizing High-quality Participation From Federated Learning Agents write newline

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T19:26:45.693146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers