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Hierarchically Fair Federated Learning
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When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded. To encourage the application of federated learning, this paper employs a management strategy, i.e., more contributions should lead to more rewards. We propose a novel hierarchically fair federated learning (HFFL) framework. Under this framework, agents are rewarded in proportion to their pre-negotiated contribution levels. HFFL+ extends this to incorporate heterogeneous models. Theoretical analysis and empirical evaluation on several datasets confirm the efficacy of our frameworks in upholding fairness and thus facilitating federated learning in the competitive settings.
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Cited by 1 Pith paper
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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks
Aequa allocates model widths (and thus accuracies) to federated learning participants in proportion to their estimated contributions, using slimmable networks and a simulated annealing optimizer.
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