FedVal: Different good or different bad in federated learning
Viktor Valadi Xinchi Qiu Pedro Porto Buarque de Gusmão Nicholas D. Lane Mina Alibeigi
Federated learning (FL) systems are susceptible to attacks from malicious
actors who might attempt to corrupt the training model through various
poisoning attacks. FL also poses new challenges in addressing group bias, such
as ensuring fair performance for different demographic groups. Traditional
methods used to address such biases require centralized access to the data,
which FL systems do not have. In this paper, we present a novel approach FedVal
for both robustness and fairness that does not require any additional
information from clients that could raise privacy concerns and consequently
compromise the integrity of the FL system. To this end, we propose an
innovative score function based on a server-side validation method that
assesses client updates and determines the optimal aggregation balance between
locally-trained models. Our research shows that this approach not only provides
solid protection against poisoning attacks but can also be used to reduce group
bias and subsequently promote fairness while maintaining the system's
capability for differential privacy. Extensive experiments on the CIFAR-10,
FEMNIST, and PUMS ACSIncome datasets in different configurations demonstrate
the effectiveness of our method, resulting in state-of-the-art performances. We
have proven robustness in situations where 80% of participating clients are
malicious. Additionally, we have shown a significant increase in accuracy for
underrepresented labels from 32% to 53%, and increase in recall rate for
underrepresented features from 19% to 50%.