Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed at devices model aggregation in central server. In order to enable efficient wireless data aggregation, over-the-air computation (AirComp) has recently attracted great attention. However, fading of channels produce aggregate distortions an AirComp-based ...

Abstract Text representations learned by machine learning models often encode undesirable demographic information of the user. Predictive based on these can rely such information, resulting in biased decisions. We present a novel debiasing technique, Fairness-aware Rate Maximization (FaRM), that removes protected making instances belonging to same attribute class uncorrelated, using rate-distor...