Hierarchically Fair Federated Learning
This addresses fairness in federated learning for competitive agents, but it is incremental as it builds on existing fairness concepts.
The paper tackles the problem of incentivizing self-interested agents to participate in federated learning by proposing a hierarchically fair framework that rewards agents based on their contributions, with empirical evaluation confirming its efficacy in upholding fairness.
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.