Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks | Paper Citation Record

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This paper cites and Gavra, I.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks and Gavra, I

Reference 1

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This paper cites LEAF: A Benchmark for Federated Settings.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks LEAF: A Benchmark for Federated Settings

Reference 2

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This paper cites J., Jhunjhunwala, D., Li, T., Smith, V., and Joshi, G.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks J., Jhunjhunwala, D., Li, T., Smith, V., and Joshi, G

Reference 3

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Unresolved cited work

Reference 4

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This paper cites B., Ramage, D., and Xu, Z.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks B., Ramage, D., and Xu, Z

Reference 5

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This paper cites and Kleinberg, J.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks and Kleinberg, J

Reference 6

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This paper cites Confidential Federated Computations.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Confidential Federated Computations

Reference 7

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This paper cites P., Liu, C., and Zhang, Y.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks P., Liu, C., and Zhang, Y

Reference 8

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This paper cites and Zou, J.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks and Zou, J

Reference 9

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This paper cites Profit sharing and efficiency in utility games.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Profit sharing and efficiency in utility games

Reference 10

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This paper cites Simulated annealing: A proof of convergence.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Simulated annealing: A proof of convergence

Reference 11

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This paper cites Deep residual learning for image recognition.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Deep residual learning for image recognition

Reference 12

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This paper cites Hyperparameter transfer learning with adaptive complexity.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Hyperparameter transfer learning with adaptive complexity

Reference 13

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This paper cites Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout

Reference 14

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This paper cites Maestro: Uncovering Low-Rank Structures via Trainable Decomposition.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Maestro: Uncovering Low-Rank Structures via Trainable Decomposition

Reference 15

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This paper cites Papaya: Practical, private, and scalable federated learning.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Papaya: Practical, private, and scalable federated learning

Reference 16

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This paper cites A., Hynes, N., G \"u rel, N.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks A., Hynes, N., G \"u rel, N

Reference 17

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This paper cites Fair Federated Medical Image Segmentation via Client Contribution Estimation.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Fair Federated Medical Image Segmentation via Client Contribution Estimation

Reference 18

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Unresolved cited work

Reference 19

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This paper cites Tighter theory for local sgd on identical and heterogeneous data.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Tighter theory for local sgd on identical and heterogeneous data

Reference 20

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

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Learning multiple layers of features from tiny images

Reference 21

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This paper cites Matryoshka representation learning.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Matryoshka representation learning

Reference 22

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This paper cites The mnist database of handwritten digits.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks The mnist database of handwritten digits

Reference 23

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This paper cites K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V

Reference 24

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This paper cites Ditto: Fair and robust federated learning through personalization.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Ditto: Fair and robust federated learning through personalization

Reference 25

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Unresolved cited work

Reference 26

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This paper cites A contract theory based incentive mechanism for federated learning.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks A contract theory based incentive mechanism for federated learning

Reference 27

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This paper cites Gtg-shapley: Efficient and accurate participant contribution evaluation in federated learning.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Gtg-shapley: Efficient and accurate participant contribution evaluation in federated learning

Reference 28

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This paper cites Collaborative fairness in federated learning.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Collaborative fairness in federated learning

Reference 29

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Resource-adaptive federated learning with all-in-one neural composition

Reference 31

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Masked training of neural networks with partial gradients

Reference 32

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Y., et al

Reference 33

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Estimation of Individual Device Contributions for Incentivizing Federated Learning

Reference 34

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Learning ordered representations with nested dropout

Reference 35

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Unresolved cited work

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Towards fairness-aware federated learning

Reference 37

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Fedfaim: A model performance-based fair incentive mechanism for federated learning

Reference 38

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning

Reference 39

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks D., Ng, A

Reference 40

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Local SGD Converges Fast and Communicates Little

Reference 41

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Redefining contributions: Shapley-driven federated learning

Reference 42

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This paper cites CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning , 2025 a.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning , 2025 a

Reference 43

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This paper cites FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning

Reference 44

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Progfed: Effective, communication, and computation efficient federated learning by progressive training

Reference 45

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks A Field Guide to Federated Optimization

Reference 46

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks A principled approach to data valuation for federated learning

Reference 47

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks K., Stich, S., Dai, Z., Bullins, B., Mcmahan, B., Shamir, O., and Srebro, N

Reference 48

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks M., Raskar, R., and Low, B

Reference 49

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

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 50

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This paper cites A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning

Reference 51

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks S., and Low, B

Reference 52

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This paper cites Asynchronous federated learning with incentive mechanism based on contract theory.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Asynchronous federated learning with incentive mechanism based on contract theory

Reference 53

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks AutoSlim: Towards One-Shot Architecture Search for Channel Numbers

Reference 54

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks and Huang, T

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:19:40.407316Z

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Observation 599b8833-fb7a-40da-af6a-08fab25f4bdf · outbound

This paper cites Slimmable neural networks.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Slimmable neural networks

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:19:40.393121Z

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Observation 398c8e53-18d3-41c9-ba0f-3033315c3b50 · outbound

This paper cites A learning-based incentive mechanism for federated learning.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks A learning-based incentive mechanism for federated learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:19:40.377100Z

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Observation fa797e2c-b888-464e-8a60-6770a61949c6 · outbound

This paper cites Hierarchically Fair Federated Learning.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Hierarchically Fair Federated Learning

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-08T21:19:39.510112Z

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Observation 0f05869d-715c-4e42-8951-0a419eb78eb1 · outbound

This paper cites Incentive mechanism for horizontal federated learning based on reputation and reverse auction.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Incentive mechanism for horizontal federated learning based on reputation and reverse auction

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:19:40.361518Z

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Observation 647b1028-8c31-4908-a749-f870b9b95787 · outbound

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

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Federated learning on non-iid data: A survey

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T21:19:39.451547Z

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This paper cites write newline.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks write newline

Reference 61

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