FLamma claims to balance fairness and accuracy in federated learning via a Stackelberg game with an adaptive decay factor, but the theory has derivation errors and the experiments use fixed local epochs.
AdaFed: Fair Federated Learning via Adaptive Common Descent Direction
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abstract
Federated learning (FL) is a promising technology via which some edge devices/clients collaboratively train a machine learning model orchestrated by a server. Learning an unfair model is known as a critical problem in federated learning, where the trained model may unfairly advantage or disadvantage some of the devices. To tackle this problem, in this work, we propose AdaFed. The goal of AdaFed is to find an updating direction for the server along which (i) all the clients' loss functions are decreasing; and (ii) more importantly, the loss functions for the clients with larger values decrease with a higher rate. AdaFed adaptively tunes this common direction based on the values of local gradients and loss functions. We validate the effectiveness of AdaFed on a suite of federated datasets, and demonstrate that AdaFed outperforms state-of-the-art fair FL methods.
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Incentive-Compatible Federated Learning with Stackelberg Game Modeling
FLamma claims to balance fairness and accuracy in federated learning via a Stackelberg game with an adaptive decay factor, but the theory has derivation errors and the experiments use fixed local epochs.